Nuclei detection of breast histopathology based on HOG feature and sliding window
- XIANG Lei, XU Jun
JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE). 2015, 45(1):
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A new method was presented which integrated histograms of oriented gradient (HOG) feature and sliding window for rapid, efficient and accurate detection of nuclei from high resolution pathological images. HOG feature was extracted from the training samples which include both nuclei and non-nuclei patches. The supervised classifier were trained with HOG features. The trained classifier was employed for automated nuclei detection from input patches that selected from histopathological images. During the detection, sliding window was used to select patches. In order to verify the effectiveness of the method on detecting nuclei from histopathological images, this article compared the proposed method with softmax (SM) classifier, sparse autoencoder(SAE)+SM, local binary pattern (LBP)+SM, support vector machine(SVM), HOG+SM, and HOG+SVM models. The experiments on 37 pieces of H&E staining histopathological images showed that the proposed method achieved highest precision, recall and F1-measure values, which were 71.5%, 82.3% and 76.5% respectively.