关键词: 粪便镜检图像, 图像分割, 结构张量, Chan-Vese模型, 随机决策森林

Abstract: In allusion to recognition method for the red and white cells with vague boundary in the stool microscopy images, this paper proposes a new method based on Chan-Vese model, which considers both the edge and texture information of neighboring regions. It proposes a synergetic combination 8 direction Sobel edge enhancement with the tensor field as the region texture attribute in order to remedy the clear cell’s fuzzy boundary and keep the cell’s inherent texture. The complementary edge and texture is more suitable for the Chan-Vese segmentation model considering global and local energy distribution. It also takes the random decision forest with better data generalization effect as a classification tool. The experimental results show that the improved Chan-Vese segmentation method considering both boundary and domain texture makes the segmentation accuracy of red and white cells in the stool microscopy  images up to 95.3%. This method has higher recognition precision and strong universality for tangible object in stool microscopy images.

Key words: stool microscopy images, image segmentation, structure tensor, Chan-Vese model, random decision forest