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

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.

doi:10.1117/12.597075

29 April 2005 A probabilistic approach to segmentation and classification of neoplasia in uterine cervix images using color and geometric features

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.

Author Affiliations +

Proceedings Volume 5747, Medical Imaging 2005: Image Processing; (2005) https://doi.org/10.1117/12.597075
Event: Medical Imaging, 2005, San Diego, California, United States

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.

Citation Download Citation

Yeshwanth Srinivasan, Dana Hernes, Bhakti Tulpule, Shuyu Yang, Jiangling Guo, Sunanda Mitra, Sriraja Yagneswaran, Brian Nutter, Jose Jeronimo, Benny Phillips M.D., Rodney Long, and Daron Ferris M.D. "A probabilistic approach to segmentation and classification of neoplasia in uterine cervix images using color and geometric features", Proc. SPIE 5747, Medical Imaging 2005: Image Processing, (29 April 2005); https://doi.org/10.1117/12.597075

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available