A probabilistic approach to segmentation and classification of neoplasia in uterine cervix images using color and geometric features

Abstract

Automated segmentation and classification of diagnostic markers in medical imagery are challenging tasks. Numerous algorithms for segmentation and classification based on statistical approaches of varying complexity are found in the literature. However, the design of an efficient and automated algorithm for precise classification of desired diagnostic markers is extremely image-specific. The National Library of Medicine (NLM), in collaboration with the National Cancer Institute (NCI), is creating an archive of 60,000 digitized color images of the uterine cervix. NLM is developing tools for the analysis and dissemination of these images over the Web for the study of visual features correlated with precancerous neoplasia and cancer. To enable indexing of images of the cervix, it is essential to develop algorithms for the segmentation of regions of interest, such as acetowhitened regions, and automatic identification and classification of regions exhibiting mosaicism and punctation. Success of such algorithms depends, primarily, on the selection of relevant features representing the region of interest. We present color and geometric features based statistical classification and segmentation algorithms yielding excellent identification of the regions of interest. The distinct classification of the mosaic regions from the non-mosaic ones has been obtained by clustering multiple geometric and color features of the segmented sections using various morphological and statistical approaches. Such automated classification methodologies will facilitate content-based image retrieval from the digital archive of uterine cervix and have the potential of developing an image based screening tool for cervical cancer.

An agglomerative segmentation framework for non-convex regions within uterine cervix images

“…Works that develop novel acquisition protocols, that enforce a more controlled imaging environment or enables fusion of image modalities, are starting to emerge [1,15,19]. Segmentation efforts for cervigrams within the NIH database were recently introduced [2,5,10,26], with most works relying on pixel-based clustering using color features [2,5,26]. Fig.…”

Content analysis of uterine cervix images: initial steps towards content based indexing and retrieval of cervigrams

“…Gordon et al 20 introduce initial investigation of adequate feature-spaces for automatic cervigram segmentation. With respect to a CBIR system: preliminary segmentation efforts for the cervigrams within the NCI/NLM database were recently introduced by Zimmerman et al 21 and by Srinivasan et al 22 Currently, no study exists that provides a complete analysis of the cervigram images for the content-retrieval task. Cervigrams contain complex and confusing lesion patterns, and their automatic analysis is a challenging task (for an example cervigram see Figure 1).…”

Detection and Characterization of Abnormal Vascular Patterns in Automated Cervical Image Analysis

“…However, in the experiments, the region of interest is pre-marked and automatic detection of vessels is not addressed. In [3], preliminary results are presented on detecting mosaic patterns using both color and geometric features. The method used seems to over-detect the vascular structure on both normal cervix region, and in the acetowhite regions.…”