Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Unsupervised texture segmentation using Gabor filters
Pattern Recognition
Texture Segmentation Using Fractal Dimension
IEEE Transactions on Pattern Analysis and Machine Intelligence
Probability Estimates for Multi-class Classification by Pairwise Coupling
The Journal of Machine Learning Research
Hi-index | 0.00 |
This paper presents an automated system for grading pathological images of prostatic carcinoma based on a set of texture features extracted by multi-categories of methods including multi-wavelets, Gabor-filters, GLCM, and fractal dimensions. We apply 5-fold cross-validation procedure to a set of 205 pathological prostate images for training and testing. Experimental results show that the fractal dimension (FD) feature set can achieve 92.7% of CCR without feature selection and 94.1% of CCR with feature selection by using support vector machine classifier. If features of multi-categories are considered and optimized, the CCR can be promoted to 95.6%. The CCR drops to 92.7% if FD-based features are removed from the combined feature set. Such a result suggests that features of FD category have significant contributions and should be included for consideration if features are selected from multi-categories.