AbstractIt has been shown that breast density and parenchymal patterns are significant indicators in mammographic risk assessment. In addition, studies have shown that the sensitivity of computer aided tools decreases significantly with increase in breast density. As such, mammographic density estimation and classification plays an important role in CAD systems. In this paper, we present the classification of mammographic images according to breast parenchymal structures through a multi-scale ellipse blob detection technique. Our classification is based on classifying the mammographic images of the MIAS dataset into high/low risk mammograms based on features extracted from a blob detection technique which is based on breast tissue structure. In addition, it evaluates the relation between the BIRADS classes and low/high risk mammograms. Results demonstrate the probability of estimating breast density using computer vision techniques to improve classification of mammographic images as low/high risk

Denton, Erika

George, Minu

Zwiggelaar, Reyer

English

Elsevier - Publisher Connector 

Mammographic Ellipse Modelling for Risk Estimation 

It has been shown that breast density and parenchymal patterns are significant indicators in mammographic risk assessment. In addition, studies have shown that the sensitivity of computer aided tools decreases significantly with increase in breast density. As such, mammographic density estimation and classification plays an important role in CAD systems. In this paper, we present the classification of mammographic images according to breast parenchymal structures through a multi-scale ellipse blob detection technique. Our classification is based on classifying the mammographic images of the MIAS dataset into high/low risk mammograms based on features extracted from a blob detection technique which is based on breast tissue structure. In addition, it evaluates the relation between the BIRADS classes and low/high risk mammograms. Results demonstrate the probability of estimating breast density using computer vision techniques to improve classification of mammographic images as low/high ris

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Aberystwyth UniversityMammographic Ellispe Modelling for Risk EstimationGeorge, Minu; Denton, Erika; Zwiggelaar, ReyerPublished in:Procedia Computer ScienceDOI:10.1016/j.procs.2016.07.020Publication date:2016Citation for published version (APA):George, M., Denton, E., & Zwiggelaar, R. (2016). Mammographic Ellispe Modelling for Risk Estimation. ProcediaComputer Science, 90, 163-168. https://doi.org/10.1016/j.procs.2016.07.020Document LicenseCC BY-NC-NDGeneral rightsCopyright and moral rights for the publications made accessible in the Aberystwyth Research Portal (the Institutional Repository) areretained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by thelegal requirements associated with these rights.            • Users may download and print one copy of any publication from the Aberystwyth Research Portal for the purpose of private study orresearch.            • You may not further distribute the material or use it for any profit-making activity or commercial gain            • You may freely distribute the URL identifying the publication in the Aberystwyth Research PortalTake down policyIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediatelyand investigate your claim.tel: +44 1970 62 2400email: is@aber.ac.ukDownload date: 03. Oct. 2019 Procedia Computer Science  90 ( 2016 )  163 – 168 Available online at www.sciencedirect.com1877-0509 © 2016 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).Peer-review under responsibility of the Organizing Committee of MIUA 2016doi: 10.1016/j.procs.2016.07.020 ScienceDirectInternational Conference On Medical Imaging Understanding and Analysis 2016, MIUA 2016,6-8 July 2016, Loughborough, UKMammographic Ellipse Modelling for Risk EstimationMinu Georgea,∗, Erika Dentonb, Reyer ZwiggelaaraaDepartment of Computer Science, Aberystwyth University, Aberystwyth, SY23 3DB, UKbDepartment of Radiology, Norfolk and Norwich University Hospital, Norwich, NR4 7UY, UKAbstractIt has been shown that breast density and parenchymal patterns are signiﬁcant indicators in mammographic risk assessment. Inaddition, studies have shown that the sensitivity of computer aided tools decreases signiﬁcantly with increase in breast density. Assuch, mammographic density estimation and classiﬁcation plays an important role in CAD systems. In this paper, we present theclassiﬁcation of mammographic images according to breast parenchymal structures through a multi-scale ellipse blob detectiontechnique. Our classiﬁcation is based on classifying the mammographic images of the MIAS dataset into high/low risk mammo-grams based on features extracted from a blob detection technique which is based on breast tissue structure. In addition, it evaluatesthe relation between the BIRADS classes and low/high risk mammograms. Results demonstrate the probability of estimating breastdensity using computer vision techniques to improve classiﬁcation of mammographic images as low/high risk.c© 2016 The Authors. Published by Elsevier B.V.Peer-review under responsibility of the Organizing Committee of MIUA 2016.Keywords: Breast density modelling; blob and ellipse detection;breast density classiﬁcation ; mammographic risk estimation;1. IntroductionBreast cancer is considered as a major health problem among women in western countries .It is estimated that oneamong eight women in their life span has the chance of developing breast cancer. Breast tissue density and parenchy-mal pattern structures are indicative of there being a high risk of developing breast cancer and there is a direct relationwith the probability of developing breast cancer1,2,3. The recent studies have shown that the sensitivity of computeraided tools is signiﬁcantly reduced with the increase in density of the breast tissue. A few examples of mammographicimages are illustrated in Fig. 1 clarifying various types of tissue densities as per the Breast Imaging Reporting andData System (BIRADS) density classiﬁcation. The manual assessment of breast density and/or parenchymal patternsis covered in various classiﬁcation schemes1,2,4,5 which are all correlated6.The automatic segmentation of mammo-graphic tissue and the related (BIRADS) density classiﬁcation of the mammograms has been an active research area.In general this has concentrated on the modelling and detection of dense tissue7,8,11. The review by He et al. 10 demon-∗ Corresponding author.E-mail address: mig24@aber.ac.uk 2016 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons. rg/licenses/by-nc-nd/4.0/).Peer-review under responsibility of the Organizing Committee of MIUA 2016164   Minu George et al. /  Procedia Computer Science  90 ( 2016 )  163 – 168 Fig. 1. Some example mammograms showing various BIRADS density classiﬁcation based on Birads 4th edition. (a) BIRADS 1 (entirely fattybreast tissue), (b) BIRADS 2 (scattered ﬁbro-glandular density), (c) BIRADS 3 (heterogeneously dense breast obscuring small masses), and (d)BIRADS 4 (extremely dense breast lowering the sensitivity of mammography).strated all recent approaches in ﬁnding the correlation between the breast density and its classiﬁcation methods in riskassessment. In10 they showed that the main approaches in the literature use techniques like thresholding, clustering,and statistical model building.In this paper, we demonstrate the novel approach of modelling the breast tissue : i.e. the modelling of distributionof dense tissue and fatty tissue and how this can be exploited to provide the BIRADS density classiﬁcation. This workis closely related to that of Chen et al. 9, which developed a mammographic segmentation approach using topographicmaps with an emphasis on the modelling and distribution of bright blobs in mammographic images. In section 2, thecreation of gLoG kernel is brieﬂy described. Later in section 3, the results obtained from the blob detection techniqueis discussed18. In section 4, the classiﬁcation of the mammographic images from the Mammographic Image AnalysisSociety (MIAS) database into BIRADS classes based on the features obtained from the multi-scale blob detectiontechnique is described. In addition the classiﬁcation of mammographic images into low/high risk images based onblob detection is explained. Lastly section 5 discusses the results obtained and future work.2. Blob Detection from Mammogram ImagesIn computer vision, the regions or points which are identiﬁed as either darker or brighter than the local area arereferred as blobs. The higher the contrast between the region and the surrounding tissue and the more blob-like theregion is the higher the blob probability should be. Traditional approaches have concentrated on the detection ofcircular objects for which approach such as the Hough transform 12 and Laplacian of Gaussian (LoG) ﬁltering13 havebeen used. However, a lot of regions in natural images are not truly circular and as such ellipse detection mightbe a more appropriate approach. This leads to the introduction of generalized Laplacian of Gaussian (gLoG) basedapproach, which allows for the detection of asymmetric regions and includes directional estimation for such regions14. The gLoG approach uses a set of orientation and scale dependent kernels. As with the LoG approach, the image,f (x, y) is processed with a Gaussian deﬁned by∇2[G(x, y) ∗ f (x, y)] = ∇2[G(x, y)] ∗ f (x, y) (1)where∇2G(x, y) = ∂2G∂x2+∂2G∂y2(2)165 Minu George et al. /  Procedia Computer Science  90 ( 2016 )  163 – 168 Fig. 2. A set of gLoG example kernels, where (a) {σx = 4, σy = 4}, (b) {σx = 10, σy = 10}, (c) {σx = 8, σy = 2, θ = π/2}, and (d) {σx = 12, σy =8, θ = π/4}.andG(x, y) = A.e−(ax2+2bxy+cy2) (3)In Equation3, the coeﬃcients a, b, and c controls the shape and orientation of the kernel G(x, y). the parameters aredetermined bya =cos2 θ2σ2x+sin2 θ2σ2y(4)b = − sin 2θ4σ2x+sin 2θ4σ2y(5)c =sin2 θ2σ2x+cos2 θ2σ2y(6)where σx, σy and θ are respectively the standard deviation in the horizontal direction, the standard deviation in thevertical direction, and the orientation of the Gaussian kernel. The two σx, σy values determine the scale/width of theGaussian kernel, and as there are two values this can now be asymmetrical.Some typical kernels can be found in Fig. 2.This illustrates a few possible varieties of kernels obtained while changing the orientation and scale parameters.3. Data and Experimental ResultsFor this initial study we have used the MIAS dataset15 , which contains 322 digitised MLO mammograms. Thedatabase consists of left and right MLO mammograms of 161 women. The spatial resolution of images is 50μm×50μmquantised to 8 bits. All the mammograms were classiﬁed according to the BIRADS density classiﬁcation scheme4by expert breast radiologists16. As an initial step, all the images are pre-processed and the breast area is separatedfrom the background region and from the pectoral muscle. Blob-like structures detected outside the breast area arealso removed. Subsequently, all the mammograms in the dataset were processed with Equation. 1, using a variety of{σx, σy, θ} kernels. A few typical examples can be found in Fig. 3. This shows that diﬀerent size and orientationstructures are localized and enhanced by the diﬀerent kernels. Based on the results, we identify the local maximawhich indicate the central location of each blob-like structure and thresholding is used to select the regions with higherprobability. This approach is done to extract blob-like fatty and dense structures from the mammogram images. Inorder to maintain the computational cost, repeated ﬁltering of the mammographic image with a ﬁlter of increasing sizeacross scale space is avoided. Instead downsampling of the mammographic image is performed giving more stabilityto the gLoG kernel.To exhibit the working of blob detection on various density types, mammograms from BIRADS I to BIRADSIV are taken into account and is shown by Fig. 1. The extrema of local regions in the image at diﬀerent scales for166   Minu George et al. /  Procedia Computer Science  90 ( 2016 )  163 – 168 Fig. 3. Example gLoG processing based on the mammogram shown in Fig. 1(b) and the gLoG kernels shown in Fig. 2.Fig. 4. Blobs detecting fatty tissue at multi-scale after merging based on qualitative relations for mammograms sorted from BIRADS I to BIRADSIV{σx = 10, σy = 10} are estimated. Such detected blobs represent the approximate size of blob like fatty and densetissue regions. To calculate the overall amount of blob like fatty/dense tissue structures from the image, the blobsare identiﬁed at multiple scales. All these blobs are merged together to represent the total amount of dense/fattytissue in the image. The overlapped blobs due to the closely related tissues are removed while merging the blobs atdiﬀerent scales. The overlapped and intersected blobs are removed using the above qualitative relations. The mergingprocedure starts from the largest scale to the smallest scale. The external blobs are retained. If the distance betweenthe blobs is less than the radius of the largest blob, the inner blob is eliminated. If they are intersecting and if thedistance between them is less than the radius of the largest blob, it leads to its deletion. Blob like structures detectedfor fatty tissues are represented in Fig. 4 while blob like structures detected for dense tissues are represented in Fig. 54. Density classiﬁcationIn order to ﬁnd the correlation between the BIRADS classes and the dense/fatty areas detected from blob detectionmethodology, we classiﬁed the mammograms fromMIAS database on the basis of BIRADS class. TheMIAS databasewas classiﬁed into 4 groups of BIRADS classes by an expert radiologist.To classify the MIAS mammograms basedon the BIRADS classes, we used the K-nearest neighbour classiﬁer algorithm17.The K-nearest Neighbour classiﬁer167 Minu George et al. /  Procedia Computer Science  90 ( 2016 )  163 – 168 Fig. 5. Blobs detecting dense tissue at multi-scale after merging based on qualitative relations for mammograms sorted from BIRADS I to BIRADSIV(kNN) classiﬁes a non-classiﬁed vector into ’k’ most nearest similar vectors from the training set. kNN is based on thedistance between the sample points in the feature space data sample. The classiﬁcation result by the k-NN classiﬁeris represented by Table 1.The MIAS mammographic images are classiﬁed into BIRADS classes based on the featuresobtained from blob detection.Table 1. Confusion matrix for k-NN classiﬁer based on BIRADS classesAutomatic ClassiﬁcationTruth Data Birads I Birads II Birads III Birads IVBirads I 44 21 13 9Birads II 22 37 35 9Birads III 12 36 35 10Birads IV 8 9 11 9To ﬁnd the correlation between classiﬁcation by classiﬁer on the BIRADS class and the risk, the MIAS databasewas divided into low risk and high risk mammograms based on the tissue type they have. BIRADS I and II wereconsidered low risk as they have a low amount of dense tissue while BIRADS III and IV were considered highrisk mammograms as they have more dense tissue. The same classiﬁer, k-NN algorithm was used to classify themammograms as low/high risk. It gave a classiﬁcation similar to the Birads classiﬁcation and is illustrated in Table 2.Table 2. Confusion matrices of k-NN classiﬁer for classifying low/high riskAutomatic ClassiﬁcationLow Risk High riskLow Risk 132 60High risk 65 63The method was applied on a set of 320 mammograms taken from the MIAS database.To evaluate the results, weused a 10 fold cross validation method on the entire data set. The confusion matrix for the classiﬁer is shown in Table1. The rows in the confusion matrix indicate the true class and columns indicate the label the classiﬁers associates168   Minu George et al. /  Procedia Computer Science  90 ( 2016 )  163 – 168 with the object. It was found that the accuracy of k-nn classiﬁer on classifying the mammograms as high-risk andlow-risk based on the dense area and fatty area found by the blob detection method is 61%. This is correlated to theresults obtained in Table 1 where the mammographic images are classiﬁed based on BIRADS version 44.5. Discussion and ConclusionsThe main purpose of the paper has been in determining the fatty/dense tissue present in mammograms using multi-scale blob detection method and to classify the mammograms based on the BIRADS classiﬁcation leading to theclassiﬁcation of mammographic images into high risk and low risk mammograms.We have shown that the fatty/dense tissue area obtained from the mammographic images through the generalizedLaplacian of Gaussian based approach can be used to classify the mammographic images into high/low risk mammo-grams. The comparison between the classiﬁcation of BIRADS class and high/low risk mammograms based on thefatty/dense tissue obtained from the multi-scale blob method give a linear relationship.Future work will focus on classifying the mammograms using more fatty/dense blob structures obtained from awide number of kernels at diﬀerent range of ratios of {σx and σy}. In addition, we will evaluate the classiﬁcationon larger digitised and digital datasets to estimate the relationship between dense/fatty tissue and classify them aslow/high risk and ﬁnd the relation between the Birads classes. Similarly the classiﬁcation of mammograms into highrisk and low risk will be improved through evaluating more features at diﬀerent orientations.References1. Wolfe, John.N,. Risk for breast cancer development determined by mammographic parenchymal pattern. Cancer, vol.37, pp.2486–2492,(1976).2. Boyd N.F, Byng J.W, Jong R.A, Fishell E.K, Little L.E, Miller A.B, Lockwood G.A, Tritchler D.L, Yaﬀe M.J.: Quantitative classiﬁcation ofmammographic densities and breast cancer risk: results from the Canadian National Breast Screening Study.Journal of the National CancerInstitute, vol.87,pp. 670–675 (1995).3. Vachon C.M, Van Gils C.H, Sellers T.A, Ghosh K, Pruthi S, Brandt K.R, Pankratz V.S. Mammographic density, breast cancer risk and riskprediction. Breast Cancer Research,vol.9(6),pp.1-9, 2007.4. American College of Radiology. BI-RADS Committee and American College of Radiology: Breast imaging reporting and data system.American College of Radiology (1998).5. Tabar L ,Tot T, Dean P: The art and science of early detection with mammography. Breast Cancer. Stuttgart, Germany: Thieme (2005).6. Muhimmah I , Oliver A ,Denton ERE , Pont J , Zwiggelaar R: Comparison between Wolfe, Boyd, BI-RADS and Taba´r based mammographicrisk assessment. : Lecture Notes in Computer Science, vol.4046,pp. 407. Springer, (2006).7. He W, Denton E , Zwiggelaar R: Mammographic segmentation and risk classiﬁcation using a novel binary model based bayes classiﬁer. BreastImaging. pp. 40–47 (2012).8. Chen Z , Zwiggelaar R: A modiﬁed fuzzy c-means algorithm for breast tissue density segmentation in mammograms.In: Information Technol-ogy and Applications in Biomedicine (ITAB),2010 10th IEEE International Conference on (pp. 1-4). IEEE(2010).9. Chen.Z, Oliver.A, Denton E.R.E., and Zwiggelaar.R: Automated mammographic risk classiﬁcation based on breast density estimation.InPattern Recognition and Image Analysis LNCS, vol.(7887), pp. 237–244. Springer, (2013).10. He W, Juette A, Denton E. R. E, Oliver A, Mart R and Zwiggelaar R : A Review on Automatic Mammographic Density and ParenchymalSegmentation. International Journal of Breast Cancer. International journal of breast cancer.Vol (2015),(2015)11. Chen Z, Arnau O, Denton E.R.E, Zwiggelaar R.: A multiscale blob representation of mammographic parenchymal patterns and mammographicrisk assessment. In Computer Analysis of Images and PatternsVol. 12(8),pp. 3838-3850 (2013).12. Illingworth .J, Kittler .J: A survey of the Hough transform.Computer vision, graphics, and image processing. vol.44,pp. 87–116. Elsevier(1988).13. Gonzales R, Woods R, and Eddins.S:Digital image processing using MATLAB. Pearson Education India(2004).14. Kong H, Akakin H.C, Sarma S.E:A generalized laplacian of gaussian ﬁlter for blob detection and its applications. IEEE Transactions onCybernetics. vol.43,pp.1719–1733 (2013).15. Suckling.J, Parker.J, Dance D. R, Astley S.M, Hutt I, Boggis C.R.M, Ricketts .I, Stamatakis E, Cerneaz N, Kok S.L, Taylor P, Betal D, andSavage J , : The mammographic image analysis society digital mammogram database. In: Proceedings of International Workshop DigitalMammography, pp.211–221.(1994).16. Oliver A., Freixenet J, Mart R, Pont J, Perez E, Denton E, Zwiggelaar R: A novel breast tissue density classiﬁcation methodology. IEEETransactions on Information Technology in Biomedicine, vol. 12,pp.55-65 (2008)17. Duda, Richard O, Peter E.H, David G.S. Pattern classiﬁcation. John Wiley and Sons, (2012).18. George.M, Rampun.A, Denton.E, Zwiggelaar .R :Mammographic Ellipse Modelling Towards Birads Density Classiﬁcation. In Proceedings ofthe 13th International Workshop on Breast Imaging June 19 - 22, 2016, Malm, Sweden.

Mammographic Ellispe Modelling for Risk Estimation

 Procedia Computer Science  90 ( 2016 )  163 – 168 
Available online at www.sciencedirect.com
1877-0509 © 2016 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license 
(http://creativecommons.org/licenses/by-nc-nd/4.0/).
Peer-review under responsibility of the Organizing Committee of MIUA 2016
doi: 10.1016/j.procs.2016.07.020 
ScienceDirect
International Conference On Medical Imaging Understanding and Analysis 2016, MIUA 2016,
6-8 July 2016, Loughborough, UK
Mammographic Ellipse Modelling for Risk Estimation
Minu Georgea,∗, Erika Dentonb, Reyer Zwiggelaara
aDepartment of Computer Science, Aberystwyth University, Aberystwyth, SY23 3DB, UK
bDepartment of Radiology, Norfolk and Norwich University Hospital, Norwich, NR4 7UY, UK
Abstract
It has been shown that breast density and parenchymal patterns are signiﬁcant indicators in mammographic risk assessment. In
addition, studies have shown that the sensitivity of computer aided tools decreases signiﬁcantly with increase in breast density. As
such, mammographic density estimation and classiﬁcation plays an important role in CAD systems. In this paper, we present the
classiﬁcation of mammographic images according to breast parenchymal structures through a multi-scale ellipse blob detection
technique. Our classiﬁcation is based on classifying the mammographic images of the MIAS dataset into high/low risk mammo-
grams based on features extracted from a blob detection technique which is based on breast tissue structure. In addition, it evaluates
the relation between the BIRADS classes and low/high risk mammograms. Results demonstrate the probability of estimating breast
density using computer vision techniques to improve classiﬁcation of mammographic images as low/high risk.
c© 2016 The Authors. Published by Elsevier B.V.
Peer-review under responsibility of the Organizing Committee of MIUA 2016.
Keywords: Breast density modelling; blob and ellipse detection;breast density classiﬁcation ; mammographic risk estimation;
1. Introduction
Breast cancer is considered as a major health problem among women in western countries .It is estimated that one
among eight women in their life span has the chance of developing breast cancer. Breast tissue density and parenchy-
mal pattern structures are indicative of there being a high risk of developing breast cancer and there is a direct relation
with the probability of developing breast cancer1,2,3. The recent studies have shown that the sensitivity of computer
aided tools is signiﬁcantly reduced with the increase in density of the breast tissue. A few examples of mammographic
images are illustrated in Fig. 1 clarifying various types of tissue densities as per the Breast Imaging Reporting and
Data System (BIRADS) density classiﬁcation. The manual assessment of breast density and/or parenchymal patterns
is covered in various classiﬁcation schemes1,2,4,5 which are all correlated6.The automatic segmentation of mammo-
graphic tissue and the related (BIRADS) density classiﬁcation of the mammograms has been an active research area.
In general this has concentrated on the modelling and detection of dense tissue7,8,11. The review by He et al. 10 demon-
∗ Corresponding author.
E-mail address: mig24@aber.ac.uk
 2016 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license 
(http://creativecommons. rg/licenses/by-nc-nd/4.0/).
Peer-review under responsibility of the Organizing Committee of MIUA 2016
164   Minu George et al. /  Procedia Computer Science  90 ( 2016 )  163 – 168 
Fig. 1. Some example mammograms showing various BIRADS density classiﬁcation based on Birads 4th edition. (a) BIRADS 1 (entirely fatty
breast tissue), (b) BIRADS 2 (scattered ﬁbro-glandular density), (c) BIRADS 3 (heterogeneously dense breast obscuring small masses), and (d)
BIRADS 4 (extremely dense breast lowering the sensitivity of mammography).
strated all recent approaches in ﬁnding the correlation between the breast density and its classiﬁcation methods in risk
assessment. In10 they showed that the main approaches in the literature use techniques like thresholding, clustering,
and statistical model building.
In this paper, we demonstrate the novel approach of modelling the breast tissue : i.e. the modelling of distribution
of dense tissue and fatty tissue and how this can be exploited to provide the BIRADS density classiﬁcation. This work
is closely related to that of Chen et al. 9, which developed a mammographic segmentation approach using topographic
maps with an emphasis on the modelling and distribution of bright blobs in mammographic images. In section 2, the
creation of gLoG kernel is brieﬂy described. Later in section 3, the results obtained from the blob detection technique
is discussed18. In section 4, the classiﬁcation of the mammographic images from the Mammographic Image Analysis
Society (MIAS) database into BIRADS classes based on the features obtained from the multi-scale blob detection
technique is described. In addition the classiﬁcation of mammographic images into low/high risk images based on
blob detection is explained. Lastly section 5 discusses the results obtained and future work.
2. Blob Detection from Mammogram Images
In computer vision, the regions or points which are identiﬁed as either darker or brighter than the local area are
referred as blobs. The higher the contrast between the region and the surrounding tissue and the more blob-like the
region is the higher the blob probability should be. Traditional approaches have concentrated on the detection of
circular objects for which approach such as the Hough transform 12 and Laplacian of Gaussian (LoG) ﬁltering13 have
been used. However, a lot of regions in natural images are not truly circular and as such ellipse detection might
be a more appropriate approach. This leads to the introduction of generalized Laplacian of Gaussian (gLoG) based
approach, which allows for the detection of asymmetric regions and includes directional estimation for such regions
14. The gLoG approach uses a set of orientation and scale dependent kernels. As with the LoG approach, the image,
f (x, y) is processed with a Gaussian deﬁned by
∇2[G(x, y) ∗ f (x, y)] = ∇2[G(x, y)] ∗ f (x, y) (1)
where
∇2G(x, y) = ∂
2G
∂x2
+
∂2G
∂y2
(2)
165 Minu George et al. /  Procedia Computer Science  90 ( 2016 )  163 – 168 
Fig. 2. A set of gLoG example kernels, where (a) {σx = 4, σy = 4}, (b) {σx = 10, σy = 10}, (c) {σx = 8, σy = 2, θ = π/2}, and (d) {σx = 12, σy =
8, θ = π/4}.
and
G(x, y) = A.e−(ax
2+2bxy+cy2) (3)
In Equation3, the coeﬃcients a, b, and c controls the shape and orientation of the kernel G(x, y). the parameters are
determined by
a =
cos2 θ
2σ2x
+
sin2 θ
2σ2y
(4)
b = − sin 2θ
4σ2x
+
sin 2θ
4σ2y
(5)
c =
sin2 θ
2σ2x
+
cos2 θ
2σ2y
(6)
where σx, σy and θ are respectively the standard deviation in the horizontal direction, the standard deviation in the
vertical direction, and the orientation of the Gaussian kernel. The two σx, σy values determine the scale/width of the
Gaussian kernel, and as there are two values this can now be asymmetrical.Some typical kernels can be found in Fig. 2.
This illustrates a few possible varieties of kernels obtained while changing the orientation and scale parameters.
3. Data and Experimental Results
For this initial study we have used the MIAS dataset15 , which contains 322 digitised MLO mammograms. The
database consists of left and right MLO mammograms of 161 women. The spatial resolution of images is 50μm×50μm
quantised to 8 bits. All the mammograms were classiﬁed according to the BIRADS density classiﬁcation scheme4
by expert breast radiologists16. As an initial step, all the images are pre-processed and the breast area is separated
from the background region and from the pectoral muscle. Blob-like structures detected outside the breast area are
also removed. Subsequently, all the mammograms in the dataset were processed with Equation. 1, using a variety of
{σx, σy, θ} kernels. A few typical examples can be found in Fig. 3. This shows that diﬀerent size and orientation
structures are localized and enhanced by the diﬀerent kernels. Based on the results, we identify the local maxima
which indicate the central location of each blob-like structure and thresholding is used to select the regions with higher
probability. This approach is done to extract blob-like fatty and dense structures from the mammogram images. In
order to maintain the computational cost, repeated ﬁltering of the mammographic image with a ﬁlter of increasing size
across scale space is avoided. Instead downsampling of the mammographic image is performed giving more stability
to the gLoG kernel.
To exhibit the working of blob detection on various density types, mammograms from BIRADS I to BIRADS
IV are taken into account and is shown by Fig. 1. The extrema of local regions in the image at diﬀerent scales for
166   Minu George et al. /  Procedia Computer Science  90 ( 2016 )  163 – 168 
Fig. 3. Example gLoG processing based on the mammogram shown in Fig. 1(b) and the gLoG kernels shown in Fig. 2.
Fig. 4. Blobs detecting fatty tissue at multi-scale after merging based on qualitative relations for mammograms sorted from BIRADS I to BIRADS
IV
{σx = 10, σy = 10} are estimated. Such detected blobs represent the approximate size of blob like fatty and dense
tissue regions. To calculate the overall amount of blob like fatty/dense tissue structures from the image, the blobs
are identiﬁed at multiple scales. All these blobs are merged together to represent the total amount of dense/fatty
tissue in the image. The overlapped blobs due to the closely related tissues are removed while merging the blobs at
diﬀerent scales. The overlapped and intersected blobs are removed using the above qualitative relations. The merging
procedure starts from the largest scale to the smallest scale. The external blobs are retained. If the distance between
the blobs is less than the radius of the largest blob, the inner blob is eliminated. If they are intersecting and if the
distance between them is less than the radius of the largest blob, it leads to its deletion. Blob like structures detected
for fatty tissues are represented in Fig. 4 while blob like structures detected for dense tissues are represented in Fig. 5
4. Density classiﬁcation
In order to ﬁnd the correlation between the BIRADS classes and the dense/fatty areas detected from blob detection
methodology, we classiﬁed the mammograms fromMIAS database on the basis of BIRADS class. TheMIAS database
was classiﬁed into 4 groups of BIRADS classes by an expert radiologist.To classify the MIAS mammograms based
on the BIRADS classes, we used the K-nearest neighbour classiﬁer algorithm17.The K-nearest Neighbour classiﬁer
167 Minu George et al. /  Procedia Computer Science  90 ( 2016 )  163 – 168 
Fig. 5. Blobs detecting dense tissue at multi-scale after merging based on qualitative relations for mammograms sorted from BIRADS I to BIRADS
IV
(kNN) classiﬁes a non-classiﬁed vector into ’k’ most nearest similar vectors from the training set. kNN is based on the
distance between the sample points in the feature space data sample. The classiﬁcation result by the k-NN classiﬁer
is represented by Table 1.The MIAS mammographic images are classiﬁed into BIRADS classes based on the features
obtained from blob detection.
Table 1. Confusion matrix for k-NN classiﬁer based on BIRADS classes
Automatic Classiﬁcation
Truth Data Birads I Birads II Birads III Birads IV
Birads I 44 21 13 9
Birads II 22 37 35 9
Birads III 12 36 35 10
Birads IV 8 9 11 9
To ﬁnd the correlation between classiﬁcation by classiﬁer on the BIRADS class and the risk, the MIAS database
was divided into low risk and high risk mammograms based on the tissue type they have. BIRADS I and II were
considered low risk as they have a low amount of dense tissue while BIRADS III and IV were considered high
risk mammograms as they have more dense tissue. The same classiﬁer, k-NN algorithm was used to classify the
mammograms as low/high risk. It gave a classiﬁcation similar to the Birads classiﬁcation and is illustrated in Table 2.
Table 2. Confusion matrices of k-NN classiﬁer for classifying low/high risk
Automatic Classiﬁcation
Low Risk High risk
Low Risk 132 60
High risk 65 63
The method was applied on a set of 320 mammograms taken from the MIAS database.To evaluate the results, we
used a 10 fold cross validation method on the entire data set. The confusion matrix for the classiﬁer is shown in Table
1. The rows in the confusion matrix indicate the true class and columns indicate the label the classiﬁers associates
168   Minu George et al. /  Procedia Computer Science  90 ( 2016 )  163 – 168 
with the object. It was found that the accuracy of k-nn classiﬁer on classifying the mammograms as high-risk and
low-risk based on the dense area and fatty area found by the blob detection method is 61%. This is correlated to the
results obtained in Table 1 where the mammographic images are classiﬁed based on BIRADS version 44.
5. Discussion and Conclusions
The main purpose of the paper has been in determining the fatty/dense tissue present in mammograms using multi-
scale blob detection method and to classify the mammograms based on the BIRADS classiﬁcation leading to the
classiﬁcation of mammographic images into high risk and low risk mammograms.
We have shown that the fatty/dense tissue area obtained from the mammographic images through the generalized
Laplacian of Gaussian based approach can be used to classify the mammographic images into high/low risk mammo-
grams. The comparison between the classiﬁcation of BIRADS class and high/low risk mammograms based on the
fatty/dense tissue obtained from the multi-scale blob method give a linear relationship.
Future work will focus on classifying the mammograms using more fatty/dense blob structures obtained from a
wide number of kernels at diﬀerent range of ratios of {σx and σy}. In addition, we will evaluate the classiﬁcation
on larger digitised and digital datasets to estimate the relationship between dense/fatty tissue and classify them as
low/high risk and ﬁnd the relation between the Birads classes. Similarly the classiﬁcation of mammograms into high
risk and low risk will be improved through evaluating more features at diﬀerent orientations.
References
1. Wolfe, John.N,. Risk for breast cancer development determined by mammographic parenchymal pattern. Cancer, vol.37, pp.2486–
2492,(1976).
2. Boyd N.F, Byng J.W, Jong R.A, Fishell E.K, Little L.E, Miller A.B, Lockwood G.A, Tritchler D.L, Yaﬀe M.J.: Quantitative classiﬁcation of
mammographic densities and breast cancer risk: results from the Canadian National Breast Screening Study.Journal of the National Cancer
Institute, vol.87,pp. 670–675 (1995).
3. Vachon C.M, Van Gils C.H, Sellers T.A, Ghosh K, Pruthi S, Brandt K.R, Pankratz V.S. Mammographic density, breast cancer risk and risk
prediction. Breast Cancer Research,vol.9(6),pp.1-9, 2007.
4. American College of Radiology. BI-RADS Committee and American College of Radiology: Breast imaging reporting and data system.
American College of Radiology (1998).
5. Tabar L ,Tot T, Dean P: The art and science of early detection with mammography. Breast Cancer. Stuttgart, Germany: Thieme (2005).
6. Muhimmah I , Oliver A ,Denton ERE , Pont J , Zwiggelaar R: Comparison between Wolfe, Boyd, BI-RADS and Taba´r based mammographic
risk assessment. : Lecture Notes in Computer Science, vol.4046,pp. 407. Springer, (2006).
7. He W, Denton E , Zwiggelaar R: Mammographic segmentation and risk classiﬁcation using a novel binary model based bayes classiﬁer. Breast
Imaging. pp. 40–47 (2012).
8. Chen Z , Zwiggelaar R: A modiﬁed fuzzy c-means algorithm for breast tissue density segmentation in mammograms.In: Information Technol-
ogy and Applications in Biomedicine (ITAB),2010 10th IEEE International Conference on (pp. 1-4). IEEE(2010).
9. Chen.Z, Oliver.A, Denton E.R.E., and Zwiggelaar.R: Automated mammographic risk classiﬁcation based on breast density estimation.In
Pattern Recognition and Image Analysis LNCS, vol.(7887), pp. 237–244. Springer, (2013).
10. He W, Juette A, Denton E. R. E, Oliver A, Mart R and Zwiggelaar R : A Review on Automatic Mammographic Density and Parenchymal
Segmentation. International Journal of Breast Cancer. International journal of breast cancer.Vol (2015),(2015)
11. Chen Z, Arnau O, Denton E.R.E, Zwiggelaar R.: A multiscale blob representation of mammographic parenchymal patterns and mammographic
risk assessment. In Computer Analysis of Images and PatternsVol. 12(8),pp. 3838-3850 (2013).
12. Illingworth .J, Kittler .J: A survey of the Hough transform.Computer vision, graphics, and image processing. vol.44,pp. 87–116. Elsevier
(1988).
13. Gonzales R, Woods R, and Eddins.S:Digital image processing using MATLAB. Pearson Education India(2004).
14. Kong H, Akakin H.C, Sarma S.E:A generalized laplacian of gaussian ﬁlter for blob detection and its applications. IEEE Transactions on
Cybernetics. vol.43,pp.1719–1733 (2013).
15. Suckling.J, Parker.J, Dance D. R, Astley S.M, Hutt I, Boggis C.R.M, Ricketts .I, Stamatakis E, Cerneaz N, Kok S.L, Taylor P, Betal D, and
Savage J , : The mammographic image analysis society digital mammogram database. In: Proceedings of International Workshop Digital
Mammography, pp.211–221.(1994).
16. Oliver A., Freixenet J, Mart R, Pont J, Perez E, Denton E, Zwiggelaar R: A novel breast tissue density classiﬁcation methodology. IEEE
Transactions on Information Technology in Biomedicine, vol. 12,pp.55-65 (2008)
17. Duda, Richard O, Peter E.H, David G.S. Pattern classiﬁcation. John Wiley and Sons, (2012).
18. George.M, Rampun.A, Denton.E, Zwiggelaar .R :Mammographic Ellipse Modelling Towards Birads Density Classiﬁcation. In Proceedings of
the 13th International Workshop on Breast Imaging June 19 - 22, 2016, Malm, Sweden.


Aberystwyth UniversityMammographic Ellispe Modelling for Risk EstimationGeorge, Minu; Denton, Erika; Zwiggelaar, ReyerPublished in:Procedia Computer ScienceDOI:10.1016/j.procs.2016.07.020Publication date:2016Citation for published version (APA):George, M., Denton, E., & Zwiggelaar, R. (2016). Mammographic Ellispe Modelling for Risk Estimation. ProcediaComputer Science, 90, 163-168. https://doi.org/10.1016/j.procs.2016.07.020Document LicenseCC BY-NC-NDGeneral rightsCopyright and moral rights for the publications made accessible in the Aberystwyth Research Portal (the Institutional Repository) areretained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by thelegal requirements associated with these rights.            • Users may download and print one copy of any publication from the Aberystwyth Research Portal for the purpose of private study orresearch.            • You may not further distribute the material or use it for any profit-making activity or commercial gain            • You may freely distribute the URL identifying the publication in the Aberystwyth Research PortalTake down policyIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediatelyand investigate your claim.tel: +44 1970 62 2400email: is@aber.ac.ukDownload date: 09. Jul. 2020 Procedia Computer Science  90 ( 2016 )  163 – 168 Available online at www.sciencedirect.com1877-0509 © 2016 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).Peer-review under responsibility of the Organizing Committee of MIUA 2016doi: 10.1016/j.procs.2016.07.020 ScienceDirectInternational Conference On Medical Imaging Understanding and Analysis 2016, MIUA 2016,6-8 July 2016, Loughborough, UKMammographic Ellipse Modelling for Risk EstimationMinu Georgea,∗, Erika Dentonb, Reyer ZwiggelaaraaDepartment of Computer Science, Aberystwyth University, Aberystwyth, SY23 3DB, UKbDepartment of Radiology, Norfolk and Norwich University Hospital, Norwich, NR4 7UY, UKAbstractIt has been shown that breast density and parenchymal patterns are significant indicators in mammographic risk assessment. Inaddition, studies have shown that the sensitivity of computer aided tools decreases significantly with increase in breast density. Assuch, mammographic density estimation and classification plays an important role in CAD systems. In this paper, we present theclassification of mammographic images according to breast parenchymal structures through a multi-scale ellipse blob detectiontechnique. Our classification is based on classifying the mammographic images of the MIAS dataset into high/low risk mammo-grams based on features extracted from a blob detection technique which is based on breast tissue structure. In addition, it evaluatesthe relation between the BIRADS classes and low/high risk mammograms. Results demonstrate the probability of estimating breastdensity using computer vision techniques to improve classification of mammographic images as low/high risk.c© 2016 The Authors. Published by Elsevier B.V.Peer-review under responsibility of the Organizing Committee of MIUA 2016.Keywords: Breast density modelling; blob and ellipse detection;breast density classification ; mammographic risk estimation;1. IntroductionBreast cancer is considered as a major health problem among women in western countries .It is estimated that oneamong eight women in their life span has the chance of developing breast cancer. Breast tissue density and parenchy-mal pattern structures are indicative of there being a high risk of developing breast cancer and there is a direct relationwith the probability of developing breast cancer1,2,3. The recent studies have shown that the sensitivity of computeraided tools is significantly reduced with the increase in density of the breast tissue. A few examples of mammographicimages are illustrated in Fig. 1 clarifying various types of tissue densities as per the Breast Imaging Reporting andData System (BIRADS) density classification. The manual assessment of breast density and/or parenchymal patternsis covered in various classification schemes1,2,4,5 which are all correlated6.The automatic segmentation of mammo-graphic tissue and the related (BIRADS) density classification of the mammograms has been an active research area.In general this has concentrated on the modelling and detection of dense tissue7,8,11. The review by He et al. 10 demon-∗ Corresponding author.E-mail address: mig24@aber.ac.uk 2016 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons. rg/licenses/by-nc-nd/4.0/).Peer-review under responsibility of the Organizing Committee of MIUA 2016164   Minu George et al. /  Procedia Computer Science  90 ( 2016 )  163 – 168 Fig. 1. Some example mammograms showing various BIRADS density classification based on Birads 4th edition. (a) BIRADS 1 (entirely fattybreast tissue), (b) BIRADS 2 (scattered fibro-glandular density), (c) BIRADS 3 (heterogeneously dense breast obscuring small masses), and (d)BIRADS 4 (extremely dense breast lowering the sensitivity of mammography).strated all recent approaches in finding the correlation between the breast density and its classification methods in riskassessment. In10 they showed that the main approaches in the literature use techniques like thresholding, clustering,and statistical model building.In this paper, we demonstrate the novel approach of modelling the breast tissue : i.e. the modelling of distributionof dense tissue and fatty tissue and how this can be exploited to provide the BIRADS density classification. This workis closely related to that of Chen et al. 9, which developed a mammographic segmentation approach using topographicmaps with an emphasis on the modelling and distribution of bright blobs in mammographic images. In section 2, thecreation of gLoG kernel is briefly described. Later in section 3, the results obtained from the blob detection techniqueis discussed18. In section 4, the classification of the mammographic images from the Mammographic Image AnalysisSociety (MIAS) database into BIRADS classes based on the features obtained from the multi-scale blob detectiontechnique is described. In addition the classification of mammographic images into low/high risk images based onblob detection is explained. Lastly section 5 discusses the results obtained and future work.2. Blob Detection from Mammogram ImagesIn computer vision, the regions or points which are identified as either darker or brighter than the local area arereferred as blobs. The higher the contrast between the region and the surrounding tissue and the more blob-like theregion is the higher the blob probability should be. Traditional approaches have concentrated on the detection ofcircular objects for which approach such as the Hough transform 12 and Laplacian of Gaussian (LoG) filtering13 havebeen used. However, a lot of regions in natural images are not truly circular and as such ellipse detection mightbe a more appropriate approach. This leads to the introduction of generalized Laplacian of Gaussian (gLoG) basedapproach, which allows for the detection of asymmetric regions and includes directional estimation for such regions14. The gLoG approach uses a set of orientation and scale dependent kernels. As with the LoG approach, the image,f (x, y) is processed with a Gaussian defined by∇2[G(x, y) ∗ f (x, y)] = ∇2[G(x, y)] ∗ f (x, y) (1)where∇2G(x, y) = ∂2G∂x2+∂2G∂y2(2)165 Minu George et al. /  Procedia Computer Science  90 ( 2016 )  163 – 168 Fig. 2. A set of gLoG example kernels, where (a) {σx = 4, σy = 4}, (b) {σx = 10, σy = 10}, (c) {σx = 8, σy = 2, θ = π/2}, and (d) {σx = 12, σy =8, θ = π/4}.andG(x, y) = A.e−(ax2+2bxy+cy2) (3)In Equation3, the coefficients a, b, and c controls the shape and orientation of the kernel G(x, y). the parameters aredetermined bya =cos2 θ2σ2x+sin2 θ2σ2y(4)b = − sin 2θ4σ2x+sin 2θ4σ2y(5)c =sin2 θ2σ2x+cos2 θ2σ2y(6)where σx, σy and θ are respectively the standard deviation in the horizontal direction, the standard deviation in thevertical direction, and the orientation of the Gaussian kernel. The two σx, σy values determine the scale/width of theGaussian kernel, and as there are two values this can now be asymmetrical.Some typical kernels can be found in Fig. 2.This illustrates a few possible varieties of kernels obtained while changing the orientation and scale parameters.3. Data and Experimental ResultsFor this initial study we have used the MIAS dataset15 , which contains 322 digitised MLO mammograms. Thedatabase consists of left and right MLO mammograms of 161 women. The spatial resolution of images is 50μm×50μmquantised to 8 bits. All the mammograms were classified according to the BIRADS density classification scheme4by expert breast radiologists16. As an initial step, all the images are pre-processed and the breast area is separatedfrom the background region and from the pectoral muscle. Blob-like structures detected outside the breast area arealso removed. Subsequently, all the mammograms in the dataset were processed with Equation. 1, using a variety of{σx, σy, θ} kernels. A few typical examples can be found in Fig. 3. This shows that different size and orientationstructures are localized and enhanced by the different kernels. Based on the results, we identify the local maximawhich indicate the central location of each blob-like structure and thresholding is used to select the regions with higherprobability. This approach is done to extract blob-like fatty and dense structures from the mammogram images. Inorder to maintain the computational cost, repeated filtering of the mammographic image with a filter of increasing sizeacross scale space is avoided. Instead downsampling of the mammographic image is performed giving more stabilityto the gLoG kernel.To exhibit the working of blob detection on various density types, mammograms from BIRADS I to BIRADSIV are taken into account and is shown by Fig. 1. The extrema of local regions in the image at different scales for166   Minu George et al. /  Procedia Computer Science  90 ( 2016 )  163 – 168 Fig. 3. Example gLoG processing based on the mammogram shown in Fig. 1(b) and the gLoG kernels shown in Fig. 2.Fig. 4. Blobs detecting fatty tissue at multi-scale after merging based on qualitative relations for mammograms sorted from BIRADS I to BIRADSIV{σx = 10, σy = 10} are estimated. Such detected blobs represent the approximate size of blob like fatty and densetissue regions. To calculate the overall amount of blob like fatty/dense tissue structures from the image, the blobsare identified at multiple scales. All these blobs are merged together to represent the total amount of dense/fattytissue in the image. The overlapped blobs due to the closely related tissues are removed while merging the blobs atdifferent scales. The overlapped and intersected blobs are removed using the above qualitative relations. The mergingprocedure starts from the largest scale to the smallest scale. The external blobs are retained. If the distance betweenthe blobs is less than the radius of the largest blob, the inner blob is eliminated. If they are intersecting and if thedistance between them is less than the radius of the largest blob, it leads to its deletion. Blob like structures detectedfor fatty tissues are represented in Fig. 4 while blob like structures detected for dense tissues are represented in Fig. 54. Density classificationIn order to find the correlation between the BIRADS classes and the dense/fatty areas detected from blob detectionmethodology, we classified the mammograms fromMIAS database on the basis of BIRADS class. TheMIAS databasewas classified into 4 groups of BIRADS classes by an expert radiologist.To classify the MIAS mammograms basedon the BIRADS classes, we used the K-nearest neighbour classifier algorithm17.The K-nearest Neighbour classifier167 Minu George et al. /  Procedia Computer Science  90 ( 2016 )  163 – 168 Fig. 5. Blobs detecting dense tissue at multi-scale after merging based on qualitative relations for mammograms sorted from BIRADS I to BIRADSIV(kNN) classifies a non-classified vector into ’k’ most nearest similar vectors from the training set. kNN is based on thedistance between the sample points in the feature space data sample. The classification result by the k-NN classifieris represented by Table 1.The MIAS mammographic images are classified into BIRADS classes based on the featuresobtained from blob detection.Table 1. Confusion matrix for k-NN classifier based on BIRADS classesAutomatic ClassificationTruth Data Birads I Birads II Birads III Birads IVBirads I 44 21 13 9Birads II 22 37 35 9Birads III 12 36 35 10Birads IV 8 9 11 9To find the correlation between classification by classifier on the BIRADS class and the risk, the MIAS databasewas divided into low risk and high risk mammograms based on the tissue type they have. BIRADS I and II wereconsidered low risk as they have a low amount of dense tissue while BIRADS III and IV were considered highrisk mammograms as they have more dense tissue. The same classifier, k-NN algorithm was used to classify themammograms as low/high risk. It gave a classification similar to the Birads classification and is illustrated in Table 2.Table 2. Confusion matrices of k-NN classifier for classifying low/high riskAutomatic ClassificationLow Risk High riskLow Risk 132 60High risk 65 63The method was applied on a set of 320 mammograms taken from the MIAS database.To evaluate the results, weused a 10 fold cross validation method on the entire data set. The confusion matrix for the classifier is shown in Table1. The rows in the confusion matrix indicate the true class and columns indicate the label the classifiers associates168   Minu George et al. /  Procedia Computer Science  90 ( 2016 )  163 – 168 with the object. It was found that the accuracy of k-nn classifier on classifying the mammograms as high-risk andlow-risk based on the dense area and fatty area found by the blob detection method is 61%. This is correlated to theresults obtained in Table 1 where the mammographic images are classified based on BIRADS version 44.5. Discussion and ConclusionsThe main purpose of the paper has been in determining the fatty/dense tissue present in mammograms using multi-scale blob detection method and to classify the mammograms based on the BIRADS classification leading to theclassification of mammographic images into high risk and low risk mammograms.We have shown that the fatty/dense tissue area obtained from the mammographic images through the generalizedLaplacian of Gaussian based approach can be used to classify the mammographic images into high/low risk mammo-grams. The comparison between the classification of BIRADS class and high/low risk mammograms based on thefatty/dense tissue obtained from the multi-scale blob method give a linear relationship.Future work will focus on classifying the mammograms using more fatty/dense blob structures obtained from awide number of kernels at different range of ratios of {σx and σy}. In addition, we will evaluate the classificationon larger digitised and digital datasets to estimate the relationship between dense/fatty tissue and classify them aslow/high risk and find the relation between the Birads classes. Similarly the classification of mammograms into highrisk and low risk will be improved through evaluating more features at different orientations.References1. Wolfe, John.N,. Risk for breast cancer development determined by mammographic parenchymal pattern. Cancer, vol.37, pp.2486–2492,(1976).2. Boyd N.F, Byng J.W, Jong R.A, Fishell E.K, Little L.E, Miller A.B, Lockwood G.A, Tritchler D.L, Yaffe M.J.: Quantitative classification ofmammographic densities and breast cancer risk: results from the Canadian National Breast Screening Study.Journal of the National CancerInstitute, vol.87,pp. 670–675 (1995).3. Vachon C.M, Van Gils C.H, Sellers T.A, Ghosh K, Pruthi S, Brandt K.R, Pankratz V.S. Mammographic density, breast cancer risk and riskprediction. Breast Cancer Research,vol.9(6),pp.1-9, 2007.4. American College of Radiology. BI-RADS Committee and American College of Radiology: Breast imaging reporting and data system.American College of Radiology (1998).5. Tabar L ,Tot T, Dean P: The art and science of early detection with mammography. Breast Cancer. Stuttgart, Germany: Thieme (2005).6. Muhimmah I , Oliver A ,Denton ERE , Pont J , Zwiggelaar R: Comparison between Wolfe, Boyd, BI-RADS and Taba´r based mammographicrisk assessment. : Lecture Notes in Computer Science, vol.4046,pp. 407. Springer, (2006).7. He W, Denton E , Zwiggelaar R: Mammographic segmentation and risk classification using a novel binary model based bayes classifier. BreastImaging. pp. 40–47 (2012).8. Chen Z , Zwiggelaar R: A modified fuzzy c-means algorithm for breast tissue density segmentation in mammograms.In: Information Technol-ogy and Applications in Biomedicine (ITAB),2010 10th IEEE International Conference on (pp. 1-4). IEEE(2010).9. Chen.Z, Oliver.A, Denton E.R.E., and Zwiggelaar.R: Automated mammographic risk classification based on breast density estimation.InPattern Recognition and Image Analysis LNCS, vol.(7887), pp. 237–244. Springer, (2013).10. He W, Juette A, Denton E. R. E, Oliver A, Mart R and Zwiggelaar R : A Review on Automatic Mammographic Density and ParenchymalSegmentation. International Journal of Breast Cancer. International journal of breast cancer.Vol (2015),(2015)11. Chen Z, Arnau O, Denton E.R.E, Zwiggelaar R.: A multiscale blob representation of mammographic parenchymal patterns and mammographicrisk assessment. In Computer Analysis of Images and PatternsVol. 12(8),pp. 3838-3850 (2013).12. Illingworth .J, Kittler .J: A survey of the Hough transform.Computer vision, graphics, and image processing. vol.44,pp. 87–116. Elsevier(1988).13. Gonzales R, Woods R, and Eddins.S:Digital image processing using MATLAB. Pearson Education India(2004).14. Kong H, Akakin H.C, Sarma S.E:A generalized laplacian of gaussian filter for blob detection and its applications. IEEE Transactions onCybernetics. vol.43,pp.1719–1733 (2013).15. Suckling.J, Parker.J, Dance D. R, Astley S.M, Hutt I, Boggis C.R.M, Ricketts .I, Stamatakis E, Cerneaz N, Kok S.L, Taylor P, Betal D, andSavage J , : The mammographic image analysis society digital mammogram database. In: Proceedings of International Workshop DigitalMammography, pp.211–221.(1994).16. Oliver A., Freixenet J, Mart R, Pont J, Perez E, Denton E, Zwiggelaar R: A novel breast tissue density classification methodology. IEEETransactions on Information Technology in Biomedicine, vol. 12,pp.55-65 (2008)17. Duda, Richard O, Peter E.H, David G.S. Pattern classification. John Wiley and Sons, (2012).18. George.M, Rampun.A, Denton.E, Zwiggelaar .R :Mammographic Ellipse Modelling Towards Birads Density Classification. In Proceedings ofthe 13th International Workshop on Breast Imaging June 19 - 22, 2016, Malm, Sweden.

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Mammographic Ellispe Modelling for Risk Estimation

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