Abstract: Naked eye acetic acid test is an important means of cervical cancer screening, so that the automatic identification of the white area in the colposcopy equipment is an effective way to solve the problem of lack of experienced doctors in clinic. Aiming at this purpose, an improved CV model level set algorithm based on gray level co-occurrence characteristic matrix was proposed in this paper. Firstly, the cervical region was segmented by using k-means algorithm from the original post-acetic acid test cervix image. Secondly, a composite gray level co-occurrence moment characteristic was used to extract the acetowhite(AW) feature and configure the feature image to be segmented. Lastly, a modified CV model level set algorithm was used to segment the feature image and the AW region was obtained eventually.The experimental results show that the modified level set algorithm gains an average 26.6% lower sensitivity and an average 29.45% higher specificity comparing with the original CV model level set algorithm. It also gains an average 47.6% higher sensitivity and an average 11.64% lower specificity comparing with watershed algorithm and an average 11.23% higher sensitivity and an average 45.23% higher specificity comparing with fuzzy clustering algorithm. However, the developed method has an average 19.74%, an average 23.27% and an average 38.11% higher JI (Jaccard Index) accuracy separately comparing with the three aforementioned algorithms. It can be concluded that the new method is a more accurate algorithm in the overall performance.