Total views : 354
Quarter Plane ARMA Model for Analysis and Classification of Histopathology Images: Application to Cancer Detection
Objective: Cancer diagnostic using clinical pathology have been proved as a standard method in which histologist/ pathologist examines biopsy sample for cell morphology and tissue distribution. Pathologist detects random growth and random placements in tissue samples. These diagnostics are very subjective and based on experience/knowledge base of pathologists. This work presents the use of 2D Autoregressive And Moving Average (ARMA) model in computer assisted automatic cancer detection. Analysis: ARMA model parameters have been considered for representing entire histopathology image. These features have further used for analysis and classification. Parameter estimation has been carried out by Yule walker Least Square (LS) method. Histology images have been classified into healthy and malignant images according to ARMA parameters. K- Fole cross validation has been performed with Linear Kernel support vector machine classifier for classification. Findings: As an outcomes of this experimentation, it is proven that ARMA model parameters works as an excellent discriminating features. These ARMA features are capable of extracting hidden information of the underlying cancer decease. This study also presents the role of neighborhood pixel in image analysis and classification. Improvement: This work have described innovative way of using ARMA features in histopathology imagery and can be implemented in computer assisted diagnosis.
Autoregressive Model and Moving Average (ARMA), Markov Random Field model (MRF), Model Based Study, Quarter Plane (QP), Support Vector Machine Minimum (SVM), Texture Analysis, Yule Walker Least Square (YWLS).
- Cigdem Demir, Bulent Yener. Automated cancer diagnosis based on histopathological images: a systematic survey, Technical Report, Rensselaer Polytechnic Institute, Dept. of Computer Science, (TR-05-09) 2009, 1-16p.
- Dan C. Cries¸ Alessandro Giusti, Luca M. Gambardella, J. Schmidhuber. Mitosis Detection in Breast Cancer Histology Images with Deep Neural Networks, Medical Image Computing and Computer-Assisted Intervention, MICCAI 2013. 16th International Conference, Nagoya, Japan, 2013, 411-18p.
- Stathonikos N, Veta M, Huisman A, van Diest PJ. Going fully digital: Perspective of a Dutch academic pathology lab, J. Pathology and Informatics. 2013; 4(1):1-14.
- Meijer GA, Belien JA, van Diest PJ, Baak JP, Origins of image analysis in clinical pathology, Journal of Clinical Pathology. 1997; 50(5):365–70.
- Macenko M, Niethammer M, Marron JS, Borland D, Woosley JT, Guan X, Schmitt C, Thomas NE. A method for normalizing histology slides for quantitative analysis, Proceedings of IEEE International Symposium on Biomedical Image Processing (ISBI). 2009; 1107-10.
- Walker RA. Quantification of immuno histo chemistry Issues concerning methods, utility and Semi quantitative assessment, Histopathology. 2006; 49(4):406-10.
- Belsare AD, Mushrif MM. Histo pathological Image Analysis Using Image Processing Techniques: An Overview. Signal& Image Processing: An International Journal (SIPIJ). 2012 August; 3(4): 22-31.
- Metin N. Gurcan, Tony Pan, Hiro Shimada, Joel Saltz. Image Analysis for Neuroblastoma Classification: Segmentation of Cell Nucleri. Proceedings of the 28th IEEE EMBS Annual International Conference New York City, USA, Aug 30 - Sept 3, 2006.
- Chen CH, Pau LF, Wang PSP. The Handbook of Pattern Recognition and Computer Vision. World Scientific Publishing Co.1998, 2nd Edn., 207-48.
- Chellappa R. Stochastic models in image analysis and processing, Ph.D. Thesis, Purdue University, 1981.
- Robert M. Haralick, Statistical and Structural Approaches to Texture. Proceedings of the IEEE, 1979; 67(5):786-804.
- Anant Madabhushi, Bulent Yener. Current Methods of Feature Extraction for Microscopic Images. Project Report, International Research Staff Exchange Scheme, (IRSES) 2009.
- Humayun Irshad, Antoine Veillard, Ludovic Roux, Daniel Racoceanu. Methods for Nuclei Detection, Segmentation, and Classification in Digital Histopathology: A Review, Current Status and Future Potential Methodological Review, IEEE Reviews in Biomedical Engineering. 2014; 7:97-114.
- Rodenacker K, Bengtsson E. Feature set for cytometry on digitized microscopic images. Analytical Cellular Pathology. 2003; 25(1):1-36.
- Sims AJ, Bennert MK, Murray A. Image analysis can be used to detect spatial changes in the histopathology of pancreatic tumors. Physics in Machine and Biology. 2003; 48(13):183-91.
- Gill AJ, Wu H, Wang BY. Image analysis and Morphometry in the Diagnosis of Breast Cancer, Microscopy Research and Technique. 2002; 59:109-18.
- Boucheron LE. Object and Spatial level Quantitive Analysis of Multispectral Histopathology Images for Detection and Characterization of Cancers, PhD Thesis, 2008.
- Sertel O, Kong J, Catayurek U, Lozansski G, saltz J, Gurcan M. Histopathological image Analysis using model based Intermediate Representation of Color Texture: Follicular Lymphoma Grading. Journal of Signal Processing Systems. 2009; 55:169-83.
- Bilgin C, Bullough P, Plopper G, Yener B. ECM Aware Cell Graph Mining for Bone Tissue Modelling and Analysis. Renselear Polytechnic Institute, Computer Science Technical Report, 08-07, 2008.
- Gunduz C, Yener B, Gultekin S. The cell graphs of Cancer. In: Proceedings Twelfth International Conference on Intelligent Systems for Molecular Biology/Third European Conference on Computational Biology 2004 Glasgow, UK July 31-August 4, 2004, Bioinformatics. Oxford Univ. Press, 2004; 20(suppl. 1):i145-i151.
- Metin N. Gurcan, Laura E. Boucheron, Anant Madabhushi, Nasir M. Rajpoot, Bulent Yener. Histopathological Image Analysis: A Review. IEEERE Views on Biomedical Engineering. 2009; 2: 147-71.
- Ajay Nagesh Basavanthy, Shridar Ganesan, Shannon Agner, James Peter Monaco, Michael , D. Feldman, John E. Tomaszewski, Gyan Bhanot, Anant Madabhushi. Computerized Image Based Detection and Grading of Lymphocytic Infiltration in HER2 + Breast cancer Histopathology. IEEE Transactions on Biomedical Engineering. 2010; 57(3):642-53.
- Robab Sheikhpour, Nasrin Ghassemi, ParichehrehYaghmael, Javed Mohiti Ardwkani, Mustafa Shiryazd. Immuno histo chemical assessment of p53 protein and its correlation with Clinic pathological characteristics in Breast Cancer patients. Indian Journal of Science and Technology. 2014 April; 7(4):472-79.
- Vaishali D, Ramesh R, Anita Christaline J. Histopathology image analysis and classification for cancer detection using 2D Autoregressive model. International Review on Computers and Software. 10(2); 182-88.
- Rajeev Srivastava, Sharma S, Singh SK. Design, analysis and classifier evaluation for CAD tool for breast cancer detection from digital mammograms. International journal of Biomedical Engineering and Technology. 2013; 13(1):270-300.
- Muthu Rama Krishnan M, 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. Journal of Computers in Biology & Medicine. 2009; 39:1096-1104.
- Scott Doyle, Michael Feldman, John Tomaszewski, Anant Madabhushi. A Boosted Bayesian Multi-Resolution Classifier for prostate cancer Detection from digitized needle biopsies. Transaction on Biomedical Engineering. 2005; 59(05): 2005-18.
- Padmapriya S, Kirubakaran E, Elango NM. Medical image classification using hybrid classifier by extending the attributed. Indian Journal of Science & Technology. 2016 Feb; 9(6):1-5. 17485/ijst/2016/v9i6/84772.
- Deepa SN, Aruna Devi B. A Survey on Artificial Intelligence Approaches for Medical Image Classification. Indian Journal of Science and Technology. 2011 Nov; 4(11):1583-95.
- Murat Dundar , Sunil Badve, Gokhan Bilgin, Vikas Raykar, Rohit Jain, Olcay Sertel, Metin N. Gurcan. Computerized Classification of Intraductal Breast Lesions using Histopathological Images. IEEE Transaction on Biomedical Engineering. 2011; 58(7):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. 2009; 42(6):1104-12.
- Kannan KP, Ananthakumarari A.Texture Analysis of Histopathology Images to identify Anomalous Region, International Journal of Management, IT and Engineering. 2012; 2(8):1-10.
- Omar Al-Kadi. Texture measures combination for improved meningioma classification of histopathology images. Journal of Pattern Recognition. 2010; 43(6):2043-53.
- Hui Kong, Metin Gurcan, Kamel Belkacern-Boussaid. Partitioning Histopathological images: An Integrated framework for Supervised color-texture Segmentation and Cell Splitting. IEEE Transactions on Medical Imaging. 2011; 30(9):1661-77.
- Rangasami L. Kashyap, Ramlingam Chellappa. Texture Synthesis Using 2-D Non causal Autoregressive Models. IEEE Transaction on Acoustics, Speech and Signal Processing. 1985; 33(1): 194-203.
- Hawkins JK. Textural properties for pattern recognition, in Bernice Sacks Linkman and Azriel Rosenfeld (EDS). Picture Processing and Psychopictorics, New York: Academic Press, 1969, 347-70.
- McCormick H, Jayaramamurthy SN. Time series model for texture synthesis. Int J. Computer. Inform. S&IP;. 1974; 3(4):329-43.
- Koichiro Deguchi. Two dimensional Autoregressive model for Analysis and Synthesis of Gray level Textures. Proceedings of the First International Symposium. 1986; 441-49.
- Tou JT, Chang YS. An approach to texture pattern analysis and recognition. Proceedings of IEEE Conf. on Decision and Control, 1-3 Dec.1976, 398-403.
- Oscar Bustos, Silvia Ojeda, Ronny Vallejos. Special ARMA Models and Its Applications in Image processing, Brazilian Journal on Probability and Statistics. 2009; 23(2):141-65.
- Gray RM. Toeplitz and circulant matrices: A review. Found.Trends Commun. Inf. Theory. 2006; 2(3): 155-239.
- Ramin Nateghi, Habibollah Danyali, Mohammad Sadegh, Helfroush, AshkanTashk. Intelligent CAD system for Automatic Detection of Mitotic Cells from breast cancer Histology Slide Images Based on teaching-Learning Based Optimization. Computational Biology Journal. 2014; 1-9. Art. ID. 970898
- Stanford Tissue Microarray Database. https://tma.im. Date Accessed: 04/07/2015
- There are currently no refbacks.
This work is licensed under a Creative Commons Attribution 3.0 License.