Pattern recognition in histopathological image analysis requires new techniques and methods. Various techniques have been presented and some state of the art techniques have been applied to complex textural data in histological images. In this paper, we compare the novel Adaptive Discriminant Wavelet Packet Transform (ADWPT) with a few prominent techniques in texture analysis namely Local Binary Patterns (LBP), Grey Level Co-occurrence Matrices (GLCMs) and Gabor Transforms. We show that ADWPT is a better technique for Meningioma subtype classification and produces classification accuracies of as high as 90%

Qureshi, Hammad A.

Rajpoot, Nasir M. (Nasir Mahmood)

English

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http://wrap.warwick.ac.uk/            Original citation: Qureshi, H. and Rajpoot, Nasir M. (Nasir Mahmood) (2010) Comparative analysis of spatial and transform domain methods for meningioma subtype classification. In: Medical Image Understanding and Analysis (MIUA 2010), Coventry, UK, 6-7 July 2010. Published in: Proceedings of Medical Image Understanding and Analysis 2010 pp. 209-213.  Permanent WRAP url: http://wrap.warwick.ac.uk/47475       Copyright and reuse: The Warwick Research Archive Portal (WRAP) makes this work by researchers of the University of Warwick available open access under the following conditions.  Copyright © and all moral rights to the version of the paper presented here belong to the individual author(s) and/or other copyright owners.  To the extent reasonable and practicable the material made available in WRAP has been checked for eligibility before being made available.  Copies of full items can be used for personal research or study, educational, or not-for-profit purposes without prior permission or charge.  Provided that the authors, title and full bibliographic details are credited, a hyperlink and/or URL is given for the original metadata page and the content is not changed in any way.  A note on versions: The version presented in WRAP is the published version or, version of record, and may be cited as it appears here.  For more information, please contact the WRAP Team at: publications@warwick.ac.uk  HAMMAD QURESHI, NASIR RAJPOOT: 1Comparative Analysis of Spatial andTransform Domain Methods for MeningiomaSubtype ClassificationHammad Qureshi School of Elect. Eng. & Comp. Science,NUST, H-12, Islamabad, Pakistan.Nasir Rajpoot Dep. of Comp. Sc., Univ. of Warwick,Coventry, CV4 7AL, United Kingdom.AbstractPattern recognition in histopathological image analysis requires new techniques andmethods. Various techniques have been presented and some state of the art techniqueshave been applied to complex textural data in histological images. In this paper, wecompare the novel Adaptive Discriminant Wavelet Packet Transform (ADWPT) with afew prominent techniques in texture analysis namely Local Binary Patterns (LBP), GreyLevel Co-occurrence Matrices (GLCMs) and Gabor Transforms. We show that ADWPTis a better technique for Meningioma subtype classification and produces classificationaccuracies of as high as 90%.1 IntroductionMeningioma subtype classification is a real-world problem from the domain of HistologicalImage Analysis. Meningiomas are tumours of the Meninges (covering of the brain and thenervous system). Histological images are real world data and are considerably different fromsynthetic textural data. Histological images have a uniquely complex texture which repre-sents a new set of issues. The texture in histological images such as Meningiomas is moreor less non-homogenous i.e. different areas in an image may have different textural proper-ties which in turn may represent different patterns. Hence, textural analysis and subsequentrecognition is not straightforward. Moreover, intra-class variation amongst the samples be-longing to the same class is high and to make matters worse inter-class differences amongstthe samples is low. This could be seen in the Meningioma subtype images depicted in theFigure 1.Diagnosis of Meningiomas is still carried out by human experts. Its hampered by the factthat the reviewing of the histological slides is time consuming, prone to error and the inter-rater variability amongst the experts is considerable [2] which makes the therapy regimensbiased. Definition of diagnostic criterion for all tumour entities within the World HealthOrganization (WHO) Classification of Tumours [4] has been problematic. Hence, there isa need for an automated computer based technique to introduce more objectivity in to theanalysis. Most Meningiomas are benign [8] which means that neuropathologists are spend-ing most of their time analysing and diagnosing benign tumours. Consequently, their is anurgent need to develop automated techniques to aid the neuropathologist.c© 2010. The copyright of this document resides with its authors.It may be distributed unchanged freely in print or electronic forms.2 HAMMAD QURESHI, NASIR RAJPOOT:a. b.c. d.Figure 1: Various Meningioma Images belonging to each subtype a. Meningiothelial, b.Fibroblastic, c. Transitional, d. PsammomatoesSome of the results on Meningioma subtype classification have been presented in [10][9] [12] [5] [11] [1]. Many techniques have been used in literature for texture classification.Randen and Husoy [13] presented a paper on comparing various texture analysis techniquesfor Brodatz texture classification. In this paper we compare the novel Adaptive Discrimi-nant Wavelet Packet Transform (ADWPT) with Gray Level Co-occurrence Matrix (GLCM),Gabor Transform (GT) and Local Binary Patterns (LBPs) for Meningioma subtype classifi-cation. This paper presents comparative results between these techniques.2 Methods2.1 Gabor TransformGabor analysis of the textures was carried out as proposed by Ma and Manjunath [6]. Fourscales and six orientations were used to provide texture representations at various scales andorientations. Energy feature is used to construct the feature set. The mean and variance assuggested by Ma and Manjunath was also computed and classification results generated.2.2 Local Binary PatternsLBP [7] with a radius of 1 and 8 neighbourhood pixels was used in the analysis. Other radiiand number of pixels were also used with no apparent improvement in results.2.3 Adaptive Discriminant Wavelet Packet TransformADWPT was carried out up to the fourth level. The subband selection for the most discrim-inant decomposition was obtained using the Fisher Discriminant. A detailed discussion ofADWPT is presented in [10] and [11].2.4 Gray Level Co-occurrence Matrix (GLCM)GLCM analysis was carried for four directions i.e. 0o, 45o, 90o and 135o with distances setfrom 1 to 5. This generated 20 GLCM matrices for each image.HAMMAD QURESHI, NASIR RAJPOOT: 32.5 Classification using Support Vector Machines (SVMs)A gaussian kernel is used and a search for the best parameter is carried out. Matlab versionof SVMs [14] developed by Chang and Lin [3] are used for classification.3 Results and DiscussionFigure 2 shows the projections on the first three principal components performed after PCAanalysis of the features acquired for the two best feature sets i.e. GLCM and ADWPT re-spectively. The other figures have not been included due to lack of space. The 3D plotsshow that ADWPT performs much better than LBP, GLCM and Gabor Transform. In case ofADWPT, psammomatous is separated well with transitional also found on the edge forminga relatively separate cluster. GLCM produces comparative results to Gabor but is not able todifferentiate psammomatous well. LBP performs the worst with no clusters seen.The classification results given in Table 1 again prove that ADWPT provides the bestdifferentiation amongst the meningioma subtypes followed by Gabor and GLCM with LBPproviding the worse results. There were a total of 960 meningioma images with 240 imagesper subtype. 20% of the data is used for testing i.e. 1 patient per subtype while the rest usedfor training. Daubechies 8-tap filter was the wavelet filter used.Table 1: 5-fold cross validated classification accuracy results using Support Vector Machinesfor LBP, GLCM, Gabor Transform and ADWPT (Fishers Discriminant) (F=Fibroblastic,M=Meningiotheliamatous, P=Psammomatous, T=Transitional)Feature F M P T AvgADWPT 79 89 97 89 89GT 49.2 64.2 95 60.8 67.3GLCM 68.3 74.2 75 60 69.4LBP 12.5 65.6 66.7 70.9 53.9The results in table 1 clearly show that ADWPT performs much better than GLCM, GTand LBP for meningioma subtype classification. The selection of subbands using the AD-WPT provides a mechanism for selecting the optimal wavelet packet representation. Thisenables the extraction of good features for classification. GT and GLCM acquire classifi-cation accuracies of around 67% and 69% respectively which is lower than ADWPT. LBPprovides the worst classification accuracies of 53.9%.4 ConclusionThe paper shows that ADWPT performs much better than the two spatial analysis tech-niques namely GLCM and LBP and the spatial-frequency analysis technique namely GaborTransform included in the study. In the future we will compare the technique with spatialfrequency analysis techniques such as Short time fourier transform and the wavelet packetalgorithm implemented by Al-Kadi [1]. A more detailed analysis with various other featureand scales may be carried out for GLCMs as well.4 HAMMAD QURESHI, NASIR RAJPOOT:References[1] O.S. Al-Kadi. Texture measures combination for improved meningioma classificationof histopathological images. Pattern Recognition, 2010.[2] P.C. Burger. What is an oligodendroglioma? Brain Pathol (2002), 12:257–259, 2002.[3] Chih-Chung Chang and Chih-Jen Lin. LIBSVM: a library for support vector machines,2001. Software available at urlhttp://www.csie.ntu.edu.tw/ cjlin/libsvm.[4] P. Kleihues and W. K. Cavenee. World Health Organization Classification of Tumours.Pathology and Genetics. Tumours of the Nervous System. IARC Press, 2000.[5] B. Lessmann, V. Hans, A. Degenhard, and T. W. Nattkemper. Feature space explorationof pathology images using content-based database visualization. In Proceedings SPIEMedical Imaging, 2006.[6] W.Y. Ma and B.S. Manjunath. Texture features and learning similarity. In ComputerVision and Pattern Recognition, 1996. Proceedings CVPR ’96, 1996 IEEE ComputerSociety Conference on, pages 425–430, Jun 1996. doi: 10.1109/CVPR.1996.517107.[7] T. Ojala, M. Pietikainen, and T. Maenpaa. Multiresolution gray-scale and rotationinvariant texture classification with local binary patterns. Pattern Analysis and MachineIntelligence, IEEE Transactions on, 24(7):971–987, Jul 2002. ISSN 0162-8828. doi:10.1109/TPAMI.2002.1017623.[8] A. Perry, D.H. Gutmann, and G. Reifenberger. Molecular pathogenesis of menin-giomas. Journal of neuro-oncology, 70(2):183–202, 2004.[9] H. Qureshi, N. Rajpoot, K. Masood, and V. Hans. Classification of meningiomas us-ing discriminant wavelet packets and learning vector quantization. In Proceedings ofMedical Image Understanding and Analysis, 2006.[10] H. Qureshi, O. Sertel, N. Rajpoot, R. Wilson, and M. Gurcan. Adaptive discriminantwavelet packet transform and local binary patterns for meningioma subtype classifica-tion. In Proceedings 11th Medical Image Computing and Computer-Assisted Interven-tion (MICCAI’2008), 2008.[11] Hammad Qureshi, Nasir Rajpoot, Tim Nattkemper, and Volkmar Hans. A robust adap-tive wavelet-based method for classification of meningioma histology images. In Pro-ceedings MICCAI’2009 Workshop on Optical Tissue Image Analysis in Microscopy,Histology, and Endoscopy (OPTIMHisE), 2009.[12] Hammad Qureshi, Roland Wilson, and Nasir Rajpoot. Optimal wavelet basis forwavelet packets based meningioma subtype classification. In Proceedings 12th MedicalImage Understanding and Analysis (MIUA’2008), 2008.[13] T. Randen and J. H. Husoy. Filtering for texture classification: a comparative study.IEEE Transactions on Pattern Analysis and Machine Intelligence, 21(4):291–310,1999.[14] V. Vapnik. Statistical Learning Theory. Springer N.Y., 1998.HAMMAD QURESHI, NASIR RAJPOOT: 5Figure 2: Projections on the first 3 principal components obtained using the PCA analysis ofthe a. GLCM-based Energy features and b. ADWPT (Fisher Distance) based Energy feature-set (Fibroblastic (F), Meningiotheliamatous (M), Psammomatous (P) and Transitional (T))

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Comparative analysis of spatial and transform domain methods for meningioma subtype classification

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Comparative analysis of spatial and transform domain methods for meningioma subtype classification

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