Cervical Cell Nuclear Segmentation Method Based on Optimized MSER Algorithm - HE Yong jun; ZHANG Xue yuan; SHAO Hui li; DING Bo (Journal of Harbin University of Science and Technology, 2021)
With the development of artificial intelligence technology, the automatic reading system plays an increasingly important role in assisting the diagnosis of pathologists, improving the accuracy of pathology diagnosis and reducing labor intensity. Accurate segmentation of the nucleus is the primary factor affecting the performance of the automated reading system. Because the boundary between the nucleus, the cytoplasm and the background is unclear, and the color difference between the cells is large, the nuclear segmentation is challenged. In order to solve this problem, a method of cervical nucleus segmentation based on optimal maximum stability regions(Maximally Stable Extremal Regions, MSER) algorithm is proposed. This method first converts the image to the HSV (Hue, Saturation, Value) color space. Then, after weighted combination of S and V channels, the optimized MSER algorithm is used to obtain a coarse segmentation region with uniform gray values. The parameter segmentation method is used to perform fine segmentation. Finally, the feature extraction technique is used to extract various features from the nuclear image, and the artificial neural network classifier is trained to judge whether the result obtained after segmentation is the nucleus. Experiments show that the method can accurately segment the cervical nucleus