A Novel Analysis of Clinical Data and Image Processing Algorithms in Detection of Cervical Cancer

Cervical Cancer is one of the ubiquitous forms of cancer afflicting the female population worldwide. A Digital Colposcope is a self-illuminated powerful microscope which acquires the image of the affected cervix. The raw cervix image acquired by the colposcope is known as a cervigram. The raw cervigram is preprocessed by removing the specular reflections and then the region of interest is sought. Different novel segmentation algorithms which were proposed in our earlier papers are applied to the cervigram to detect the acetowhite tissues. In the current paper, we try to draw a correlation between Pap smear results, colposcopic findings, histopathologic biopsy results and our proposed algorithmic analysis. The significance of association is assessed by chi-square testing and the strength of association by kappa statistic. The association between impression and histology was significant, the strength of correlation was also significant. The overall kappa statistic for the concordance of the algorithm with the human experts was 0.809, while that among raters, excluding the algorithm was 0.804. The proposed algorithm had better agreement with the expert decision.