Automatic identification of clue cells in microscopic leucorrhea images based on texture features and combination of kernel functions of SVM

Abstract

Automatic identification of clue cells in microscopic leucorrhea images provides important information for evaluating gynecological diseases. Traditional manual microscopic examination of Gram-stained vaginal smears is adopted by most hospitals for identifying clue cells; however, it is both complex and time-consuming. In order to solve these problems, an automatic identification of clue cells in microscopic leucorrhea images based on machine learning is proposed in this paper. First, the Otsu threshold method is used to segment regions of interest (ROI) in image preprocessing according to the morphological features of clue cells. Then, Gabor, HOG and GLCM texture features are extracted to describe irregular edges and rough surfaces of clue cells. Finally, a SVM classifier using a hybrid kernel function by linearly weighted RBF and polynomial kernels is trained to identify clue cells rapidly and conveniently. In experiments, the method using GLCM texture features and a hybrid kernel function of SVM achieved 94.64% accuracy and 94.92% recall rate, which was better than methods using Gabor or HOG texture features and a single kernel function of SVM.