Classifying Cervix Tissue Patterns with Texture Analysis

The high false-negative rate from Papanicolaou (Pap) smears has motivated the use ofcolposcopy to perform visual inspection of the uterine cervix. It has made a tremendous impact on theability of physicians to perform accurate punch biopsies for histological analysis. However, the difficultyin training gynecologists in the recognition of pathology has hindered the full utilization of colposcopy.Colposcopic images contain complex and confusing lesion patterns. Correctly analyzing and classifyingdifferent types of tissues require substantial training. The goal of this research is to develop imageanalysis software to help physicians classify various abnormal cervical tissue patterns in colposcopicexaminations. Such software will enhance the capability of colposcopy by increasing the diagnosticaccuracy and speed, and simplifying the use of colposcopy, making its utilization more widespread andaccurate. To recognize various vascular patterns characterizing different stages of dysplasia, textureanalysis techniques will be used. We will use the texture spectrum method to represent the vascularpatterns and to design features. Our primary objectives in Phase I are to implement this technique,evaluate its performance using a series of test images, and improve it. The Phase I effort will result inprototype software for discriminating vascular patterns. In Phase II, we will refine the software, conductclinical trails, compare its performance with results from biopsies, and enhance its user-interfacecapability.