Classifying Pathological Prostate Images by Fractal Analysis
This study presents an automated system for grading pathological prostate images based on texture features of multicategories including multiwavelets, Gaborfilters, gray-level co-occurrence matrix (GLCM) and fractal dimensions. Images are classified into appropriate grades by using k-nearest neighbor (k-NN) and support vector machine (SVM) classifiers. Experimental results show that a correct classification rate (CCR) of 93.7 % (or 92.7 %) can be achieved by fractal dimension (FD) feature set by using k-NN (or SVM) classifier without feature selection. If the FD feature set is optimized, the CCR of 94.2 % (or 94.1 %) can be achieved by using k-NN (or SVM) classifier. The CCR is promoted to 94.6 % (or 95.6 %) by k-NN (or SVM) classifier if features of multicategories are applied. On the other hand, the CCR drops if the FD-based features are removed from the combined feature set of multicategories. Such a result suggests that features of FD category are not negligible and should be included for consideration for classifying pathological prostate images.
KeywordsSupport Vector Machine Fractal Dimension Feature Selection Texture Feature Support Vector Machine Classifier
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