Share Email Print

Proceedings Paper

A probabilistic approach to segmentation and classification of neoplasia in uterine cervix images using color and geometric features
Author(s): Yeshwanth Srinivasan; Dana Hernes; Bhakti Tulpule; Shuyu Yang; Jiangling Guo; Sunanda Mitra; Sriraja Yagneswaran; Brian Nutter; Jose Jeronimo; Benny Phillips M.D.; Rodney Long; Daron Ferris M.D.
Format Member Price Non-Member Price
PDF $17.00 $21.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper 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.

Paper Details

Date Published: 29 April 2005
PDF: 9 pages
Proc. SPIE 5747, Medical Imaging 2005: Image Processing, (29 April 2005); doi: 10.1117/12.597075
Show Author Affiliations
Yeshwanth Srinivasan, Texas Tech Univ. (United States)
Dana Hernes, Texas Tech Univ. (United States)
Bhakti Tulpule, Texas Tech Univ. (United States)
Shuyu Yang, Texas Tech Univ. (United States)
Jiangling Guo, Texas Tech Univ. (United States)
Sunanda Mitra, Texas Tech Univ. (United States)
Sriraja Yagneswaran, Texas Tech Univ. (United States)
Brian Nutter, Texas Tech Univ. (United States)
Jose Jeronimo, National Cancer Institute (United States)
Benny Phillips M.D., Lubbock Gynecologic Oncology Associates (United States)
Rodney Long, National Library of Medicine (United States)
Daron Ferris M.D., Medical College of Georgia (United States)

Published in SPIE Proceedings Vol. 5747:
Medical Imaging 2005: Image Processing
J. Michael Fitzpatrick; Joseph M. Reinhardt, Editor(s)

© SPIE. Terms of Use
Back to Top