Objective: To design ARMA model for the analysis of histopathology images.
Background: In traditional cancer diagnosis using clinical pathology, pathologists examine biopsy
samples and analyzes it on the basis of cell structure and tissue arrangement. This has unpredictability
in ontogenesis and in positioning.
Methods: These methods have been replaced by computer assisted diagnostics (CAD) that support
impartial judgment. This work demonstrates the influence of 2D ARMA models in the analysis and
classification of histopathology images. The parameter estimation has been done with Yule walker
Least Square (LS) method. This work describes brain histopathology image by ARMA parameters
which is further analyzed and classified. These features are classified into healthy and malignant
tissue samples. The liner kernel and RBF kernel support vector machine (SVM) classifier and Fusion
of both classifiers have been used for diagnosis.
Results: Results show that estimated ARMA parameters are excellent discriminating features for
statistical study of histopathology images and valuable in cancer diagnostics. Total accuracy improvement
is shown by fusing the out puts of linear kernel and RBF kernel classifier with Bayes
and Decision template fusion schemes.
Conclusion: This work describes new approach of using ARMA features to extract hidden information
in histopathology imagery.