Open Access Open Access  Restricted Access Subscription or Fee Access

Histopathology Image Analysis and Classification for Cancer Detection Using 2D Autoregressive Model

(*) Corresponding author

Authors' affiliations



In traditional methods of cancer diagnosis using clinical pathology, pathologists inspects biopsy samples and make diagnostic inferences. These diagnostics are based on cell morphology and tissue distribution which represents randomness in growth and/or in placement. These methods are highly subjective and sometimes lead to incorrect diagnosis. But computer assisted diagnostics (CAD) enables objective judgment that is based on huge collected database. This paper presents the use of 2D autoregressive (AR) model in automated cancer diagnosis based on histopathology. This work emphasizes the contribution of 2D autoregressive models for analysis and classification of histopathological images. Autoregressive model parameter represents feature set of histopathological image of biopsy samples removed from patients. These features are further used for analysis, synthesis and classification. Yule walker Least Square (LS) method has used for parameter estimation. The test statistics for choice of proper order of model has also been suggested in paper. It has been inferred that the proper neighborhood for a given class sample images is unique and solely depends on the properties of samples under consideration. AR parameters provide features for classification of sample in three classes like benign tissue, healthy tissue and malignant tissue.
Copyright © 2015 Praise Worthy Prize - All rights reserved.


Autoregressive Model (AR); Least Square (LS); Computer Assisted Diagnosis (CAD)

Full Text:



Cigdem Demir, Bulent Yener, Automated cancer diagnosis based on histopathological images: A systematic survey, Technical Report, Rensselaer Polytechnic Institute Dpt.. Of Computer Science,Tr-05-09

A.D.Belsare, M.M.Mushrif, Histopathological Image Analysis Using Image Processing Techniques: An Overview, Signal & Image Processing: An International Journal (SIPIJ),Vol.3, No.4, August 2012. PP.22-31

Metin N.Gurcan, Laura E. Boucheron, Anant Madabhushi,, Nasir M.Rajpoot,Bulent Yener, Histopathological Image Analysis: A Review, IEEE REVIEWS In Biomedical Engineering , Vol 2,2009,147-171.

C. H. Chen, L. F. Pau, P. S. P. Wang, The Handbook of Pattern Recognition and Computer Vision, 2nd Edition, (Eds.), pp. 207- 248, World Scientific Publishing Co. 1998.

R.Chellappa, stochastic models in image analysis and processing , Ph. D. Thesis, Purdue University, 1981.

Robert M. Hawlick, Senior Member IEEE, Statistical and Structural Approaches to Texture, Proceedings of the IEEE VOL. 67, NO. 5, MAY 1979 .

IRSES Project 247091 Reports: Current Methods of Feature Extraction for Microscopic Images.

K.Rodenacker and E.Bengtsson, A feature set for cytometry on digitized microscopicimages Analytical Cellularpathology, vol.25, pp.1-36.

A.J.Sims, M.K. Bennert, and A .Murray, Image analysis can be used to detect spatial changes in the histopathology of pancreatic tumors, Physics in Machine and Biology, vol,48,pp. N 183-N191, 2003.

J.Gill, H.Wu, and B.Y. wang., Image analysis and Morphometry in the Diagnosis of Breast Cancer, Microscopy Research and Technique, vol.59,pp.109-118,2002.

L.E.Boucheron,, Object and Spatial level Quantitive Analysis of Multispectral Histopathology Images for Detection and Characterization of cancer, In PhD Thesis, 2008.

O.Sertel,J.Kong,U.Catayurek,G.Lozansski,J.saltz, and M.Gurcan, Histopathological image Analysis using model based Intermediate Representation of Color Texture,: Follicular Lymphoma Grading, Journal of Signal Processing Systems, vol.55,pp:169-183,2009.

S.Wuchty,E.Rayasz, and A.Barabasi, The architecture of Biological networks, Complex Systems in Biomedicine, 2003.

R.Albert,T.Schinwolf,I.Baumann, and H.Harms, Three- Dimensional Image Processing for Morphometric Analysis of Epithelium Sections, Cytometry, Vol.13,pp.759-765,1992.

C.Bilgin,C.Demir, C.nagi, and B.Yener, Cell-Graph Mining for Breast Tissue Modeling and Classification, IEEE Annual Symposium on Engineering in Medicine & Biology,2007,pp. 5311-5314.

C.Bilgin, P.Bullough, G. Plopper, and B. Yener, ECM Aware Cell Graph Mining for Bone Tissue Modeling and analysis, RPI Computer Science Technical Report 08-07,2008.

C.Gunduz, B.Yener, and S.Gultekin, The cell graphs of Cancer, Bioinformatics, Vol.20, 2004.pp.145-151.

Metin N.Gurcan,Tony Pan,Hiro Shimada, and Joel Saltz, Image Analysis for Neuroblastoma Classification: Segmentation of Cell Nucler:, Proceedings of the 28th IEEE EMBS Annual International Conference New York City,USA,Aug 30-Sept 3,2006.

Ajay Nagesh Basavanthy,Shridar ganesan,Shannon Agner,James Peter Monaco, Michael , D.Feldman,John E. Tomaszewski,Gyan Bhanot, and Anant Madabhushi, Computerized Image- Based Detection and Grading of Lymphocytic Infiltration in Breast cancer Histopathology, IEEE Transactions on Biomedical Engineering, Vol.57,No. 3, March 2010,pp.642-53.

K.L.Wiend, C.P.Maier,B.K.Rutt, and M.Moussa, Invasive carcinomas and fabroadenomas of the breast: comparison of micro vessel distributions-implications for imaging modalities, Radiolology,vol,208,Aug.1998,pp.477-88.

P.H.Bartels,D.Thompson,M.B.Bibbo, and J.E.Weber, Bayesian belief networks in quantitative histopathology, Anal Quant Cyto Histo l, vol 14, Histopathological Image Analysis Review, Dec1992,pp:459-73.

P.W.Hamilton,N.Anderson,P.H.Bartels, and D.Thompson, Expert System support using Bayesian belief networks in the diagnosis of fine needle aspiration biopsy specimens of breast, Journal of Clinical ppathology,vol.4.April 1994, pp329-36.

M.Muthu Rama Krishnan ,Mousami Pal, Suneel Bomminayuni, Chandan Chkraborty ,Ranjan Rashmi Paul, Jyotirmoy Chatterjee , Ajay Roy, Automated classification of cells in the sub- epithelial connective tissue of oral sub-mucous fibrosis –An SVM based approach, J.Of.Computers in Biology & Medicine 39 (2009) ,pp: 1096-1104.

Scott Doyle , Michael Feldman, John Tomaszewski, and Anant Matabhushi, A Boosted Bayesian Multi-Resolution Classifier for prostrate cancer Detection from digitized needle biopsies, Transaction on Biomedical Engineering,vol59,no.05, 2012,2005- 2018

M.Murat Dundar,Sunil Badve, Gokhan Bilgin,Vikas Raykar,Rohit Jain,Olcay Sertel, and Metin N.Gurcan, Computerized Classification of Intraductal Breast Lesions using Histopathological images, IEEE transaction on Biomedical Engineering,vol. 58,no,7,Jully 2011,1977- 84.

Akif Baurak,Melih Kanemir,Cenk Sokmensure, Cigdem Gunduz- demir, Object Oriented Texture Analysis for unsupervised segmentation of biopsy images for cancer detection , Pattern Recognition ,Vol. 42, 2009, pp: 1104-1112.

K.P.Kannan, and A.Ananthakumarari, Texture Analysis of Histopathology Images to identify Anomalous Region, International Journal of Management ,IT and Engineering, vol 2,no.8.2012,1-10.

Senthil Kumar, T.K., Narasimhan, G., Umamaheswari, R., Texture pattern based lung nodule detection (TPLND) technique in CT images, (2014) International Review on Computers and Software (IRECOS), 9 (3), pp. 415-426.

Shobha, B., Muniraj, N., Modeling and Analysis of Magnetic Nanoparticle Biosensor with DNA and Protein Biomarkers, (2014) International Journal on Information Technology (IREIT), 2 (4), pp. 114-123.


  • There are currently no refbacks.

Please send any question about this web site to
Copyright © 2005-2023 Praise Worthy Prize