Image processing techniques for the segmentation of cervical cancer
Keywords:
Image Processing, Cervical cancer, Dual-Tree Discrete Wavelet Transform (DTDWT), Curvelet Transform, Contour Transform, K-Means clusteringAbstract
The most prevalent type of cancer in women worldwide is uterine cervical cancer. The majority of cervical cancer (CC) cases can be avoided by participating in screening programmes that look for precancerous lesions. Colposcopic cervigrams or images from digital colposcopy have been acquired in raw form. This study presents a novel framework that combines image enhancement, pre-processing, and image segmentation to identify cervical cancer. Three phases make up this framework: the Dual Tree Discrete Wavelet Transform (DTDWT) for pre-processing, the Curvelet transform and Contour Transform (CC) for image improvement, and the K-means clustering for segmentation.
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