Mammographic Ellispe Modelling for Risk Estimation

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

Comparative Study on Local Binary Patterns for Mammographic Density and Risk Scoring

Breast density is considered to be one of the major risk factors in developing breast cancer. High breast density can also affect the accuracy of mammographic abnormality detection due to the breast tissue characteristics and patterns. We reviewed variants of local binary pattern descriptors to classify breast tissue which are widely used as texture descriptors for local feature extraction. In our study, we compared the classification results for the variants of local binary patterns such as classic LBP (Local Binary Pattern), ELBP (Elliptical Local Binary Pattern), Uniform ELBP, LDP (Local Directional Pattern) and M-ELBP (Mean-ELBP). A wider comparison with alternative texture analysis techniques was studied to investigate the potential of LBP variants in density classification. In addition, we investigated the effect on classification when using descriptors for the fibroglandular disk region and the whole breast region. We also studied the effect of the Region-of-Interest (ROI) size and location, the descriptor size, and the choice of classifier. The classification results were evaluated based on the MIAS database using a ten-run ten-fold cross validation approach. The experimental results showed that the Elliptical Local Binary Pattern descriptors and Local Directional Patterns extracted most relevant features for mammographic tissue classification indicating the relevance of directional filters. Similarly, the study showed that classification of features from ROIs of the fibroglandular disk region performed better than classification based on the whole breast region

The Role Of Tissue Sound Speed As A Surrogate Marker Of Breast Density

Breast density is one of the strongest predictors of breast cancer risk as women with the densest breasts have a three- to five-fold increase in risk compared to women with the least dense breasts. Breast density is currently measured by using mammography, the current gold standard for breast imaging. There are many shortcomings to using mammography to measure breast density, including the use of ionizing radiation. Ultrasound tomography (UST) does not use ionizing radiation and can create tomographic breast sound speed images. These sound speed images are useful because breast density is proportional to sound speed. The purpose of this work was to assess the ability of UST to measure breast density and its ability to measure changes in breast density over short periods of time. A cohort of 251 patients was examined using both UST and mammography. Many different associations were found between the UST density measurement, the volume averaged sound speed, and the mammographic percent density. Additional associations were found between many other UST and mammographic imaging characteristics. UST density was found to correlate with various patient characteristics in a similar manner to mammographic density. Additionally, UST was used to examine the effects of tamoxifen on breast density. Tamoxifen has been shown to reduce mammographic density and breast cancer risk for some women. Preliminary data for 52 patients has shown promising results so far. UST density has decreased for approximately a similar percentage of patients as has been measured for mammographic density. These changes have been measured over short time frames that could not be achieved using mammography. These results show that UST\u27s ability to measure breast density is consistent with mammography, the current standard of care. UST has the potential to become a safe and effective device that can be used to reliably assess breast density and serial changes in breast density

A novel and fully automated mammographic texture analysis for risk prediction : results from two case-control studies

BACKGROUND: The percentage of mammographic dense tissue (PD) is an important risk factor for breast cancer, and there is some evidence that texture features may further improve predictive ability. However, relatively little work has assessed or validated textural feature algorithms using raw full field digital mammograms (FFDM). METHOD: A case-control study nested within a screening cohort (age 46-73 years) from Manchester UK was used to develop a texture feature risk score (264 cases diagnosed at the same time as mammogram of the contralateral breast, 787 controls) using the least absolute shrinkage and selection operator (LASSO) method for 112 features, and validated in a second case-control study from the same cohort but with cases diagnosed after the index mammogram (317 cases, 931 controls). Predictive ability was assessed using deviance and matched concordance index (mC). The ability to improve risk estimation beyond percent volumetric density (Volpara) was evaluated using conditional logistic regression. RESULTS: The strongest features identified in the training set were "sum average" based on the grey-level co-occurrence matrix at low image resolutions (original resolution 10.628 pixels per mm; downsized by factors of 16, 32 and 64), which had a better deviance and mC than volumetric PD. In the validation study, the risk score combining the three sum average features achieved a better deviance than volumetric PD (Deltachi2 = 10.55 or 6.95 if logarithm PD) and a similar mC to volumetric PD (0.58 and 0.57, respectively). The risk score added independent information to volumetric PD (Deltachi2 = 14.38, p = 0.0008). CONCLUSION: Textural features based on digital mammograms improve risk assessment beyond volumetric percentage density. The features and risk score developed need further investigation in other settings

Analysis of Dynamic Magnetic Resonance Breast Images

Dynamic Magnetic Resonance Imaging is a non-invasive technique that provides an image sequence based on dynamic information for locating lesions and investigating their structures. In this thesis we develop new methodology for analysing dynamic Magnetic Resonance image sequences of the breast. This methodology comprises an image restoration step that reduces random distortions affecting the data and an image classification step that identifies normal, benign or malignant tumoral tissues. In the first part of this thesis we present a non-parametric and a parametric approach for image restoration and classification. Both methods are developed within the Bayesian framework. A prior distribution modelling both spatial homogeneity and temporal continuity between neighbouring image pixels is employed. Statistical inference is performed by means of a Metropolis-Hastings algorithm with a specially chosen proposal distribution that out-performs other algorithms of the same family. We also provide novel procedures for estimating the hyper-parameters of the prior models and the normalizing constant so making the Bayesian methodology automatic. In the second part of this thesis we present new methodology for image classification based on deformable templates of a prototype shape. Our approach uses higher level knowledge about the tumour structure than the spatio-temporal prior distribution of our Bayesian methodology. The prototype shape is deformed to identify the structure of the malignant tumoral tissue by minimizing a novel objective function over the parameters of a set of non-affine transformations. Since these transformations can destroy the connectivity of the shape, we develop a new filter that restores connectivity without smoothing the shape. The restoration and classification results obtained from a small sample of image sequences are very encouraging. In order to validate these results on a larger sample, in the last part of the thesis we present a user friendly software package that implements our methodology