Wavelet Transform Based Automatic Lesion Detection in Cervix Images Using Active Contour

Abstract

Colposcopy is a medical diagnostic procedure to examine an illuminated, magnified view of the cervix by a colposcope. Most cases of cervical cancer can be prevented through screening programs aimed at detecting precancerous lesions. Colposcopy cervical images are acquired in raw form which contains major cervix lesions, regions outside the cervix and parts of the imaging devices such as speculum. In this study, a fully automated lesion detection method based on active contour is proposed. To detect the lesion, the active contour method requires an initial mask in the acetowhite region. In the proposed method, the initial contour is automatically obtained based on Discrete Wavelet Transform (DWT). Before feature extraction, a preprocessing method is applied to remove the irrelevant information and specular reflection from the colposcopy cervical images based on Mathematical morphology, Gaussian Mixture Modeling. Then the wavelet features are extracted and the features are used as an input to the K Nearest Neighbour (KNN) to obtain the initial mask. Segmentation results are evaluated on 240 images of colposcopy.