Automatic segmentation of cervical region in colposcopic images using K-means - Bai, Bing; Liu, Pei-Zhong; Du, Yong-Zhao; Luo, Yan-Ming (Australasian physical & engineering sciences in medicine, 2018)
Colposcopy is an important imaging modality for the detection of cervical lesions. The analysis of colposcopic images, especially the effective segmentation of the cervical region, has important clinical value in clinical application. A cervical segmentation method based on the HSV color mode is proposed, which can divide and extract the cervical region in the medical and anatomical sense. Firstly, the histogram threshold method is used to analyze the histogram ( Y ) of the colposcopic image. In order to achieve the removal of the mirror reflection pretreatment operation in the colposcopy image. Secondly, the Preprocessed RGB images is used. Then, the colposcopic image is converted into the HSV color space, and the V component is extracted using the K-means algorithm. Finally, using the area filter to smooth the edge, the segmented cervical region can be obtained. In our study, 110 standard colposcopy images, which were tagged by experts, were tested and verified. The segmentation results were analyzed and compared using dice coefficients, Jaccard coefficients, structural segmentation accuracy specificity, sensitivity, positive predictive value, and negative predictive value. Our experimental results show that the accuracy, specificity and sensitivity of the method are 87.25%, 81.99% and 96.70%, respectively. The effectiveness of the method in clinical segmentation was verified. Our study has demonstrated that cervical regional segmentation of colposcopic images based on HSV color space using K-means has high clinical utility and can help medical specialists in the diagnosis of cervical cancer.