Cervical image segmentation using active contours and evolutionary programming over temporary acetowhite patterns

Cervical Cytology or Pap Smear is the most popular technique for pre-diagnosis of cervical cancer. However, this technique has a high rate of false negative. As a consequence, it is necessary to complement it with other tests like Colposcopy. In some studies about the colposcopy test, it has been proposed that temporal changes intrinsic to the colposcopic image can be used to automatically characterize cervical lesions. In this document, a methodology to segment colposcopic images based on these temporal changes produced by the acetowhite reaction is presented in order to support to the colposcopist in the early detection of cervical cancer. This methodology consists in two stages: (1) a preprocessing stage, where several steps (extraction of time series, dimensionality reduction, classification process and a post-processing stage) are done to decrease problems or noise (specular reflection and keratosis) that can be seen as the acetowhite reaction, and (2) a segmentation stage where the active contour model (Snake) is used, changing the greedy search mechanism by a global search mechanism (evolutionary programming). The experiments were developed using only one colposcopic image and all the image sequence extracted from the colposcopy process. Results show that our methodology is a competitive tool to support to the colposcopist in the early detection of cervical cancer, providing one step towards the decrease of the mortality rate of this disease.