关键词: K-means, 特征提取, 灰度共生矩阵, 不变矩, 相关系数

Abstract: Brain tumor segmentation is important for physicians to determine the extent of tumor progression. However, due to the irregular shape of the tumor, low contrast with surrounding tissues, and the location of the tumor is not fixed, the precise segmentation of brain tumors is very difficult. The traditional [k]-means segmentation method only utilizes the image’s grayscale features, so it is difficult to accurately segment the tumor boundary. In this paper, texture features produced by gray symbiosis matrix are used and [k]-means algorithm features are combined with geometric invariant moments to segment brain MRI images. The gray co-occurrence matrix, which is defined as the joint probability distribution of pixel pairs, is a symmetric matrix. It not only reflects the comprehensive information of the image’s gray level in the adjacent directions, adjacent intervals and variation range, but also reflects the location distribution features between the same gray level pixels, which is the basis for calculating texture features. Geometric moment（invariant moment） is characterized by rotation, translation, scale, etc., which can decompose the image into finite feature values. Moreover, by comparing the invariant moment parameters of the tumor image extracted from the same patient, the degree of geometric shape change of the tumor can be obtained. Experimental results show that this method can effectively improve the segmentation accuracy of brain tumor fringe region.

Key words: K-means, feature extraction, gray level co-occurrence matrix, invariant moment, correlation coefficient