Title: Autoregressive modelling: application to mitosis detection in brain cancer histopathology

Authors: D. Vaishali; R. Ramesh; J. Anita Christaline

Addresses: Department of Electronics & Communication Engineering, SRM University, Chennai, Tamil Nadu, India ' Department of Electronics & Communication Engineering, Saveetha Engineering College, Thandlam, Tamil Nadu, India ' Department of Electronics & Communication Engineering, SRM University, Chennai, Tamil Nadu, India

Abstract: In traditional methods, cancer is diagnosed using clinical pathology, where pathologists inspect biopsy samples and make inferences. These inferences are based on cell morphology and tissue distribution which represent randomness in growth and/or in placement. Computer-Assisted Diagnostics (CAD) aids objective judgement that is based on very large database. This work emphasises the contribution of 2D autoregressive model for analysis and classification of histopathological images. Autoregressive model parameters represent a feature set of histopathological image made from biopsy samples taken from patients. These features are further used for analysis, synthesis and classification. The Yule-Walker Least-Squares (LS) method has been used for parameter estimation. AR parameters provide features for classification of sample in two classes: healthy tissue and malignant tissue. Feature data sets have been classified with Support Vector Machine (SVM) classifier. In view of cancer diagnosis, this work also explains the concepts of sensitivity, specificity and classification accuracy.

Keywords: autoregressive modelling; least squares; CAD; computer-assisted diagnosis; SVM; support vector machines; mitosis detection; brain cancer; histopathological images; image analysis; image classification; biopsy; parameter estimation; cancer diagnosis.

DOI: 10.1504/IJBET.2016.074202

International Journal of Biomedical Engineering and Technology, 2016 Vol.20 No.2, pp.179 - 194

Received: 27 Apr 2015
Accepted: 22 Jul 2015
Published online: 16 Jan 2016 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article