Improving Clinical Diagnosis through Change Detection in Mammography Sequences
Abstract
In computer-aided diagnosis, temporal change overtime can be a key piece of information in treatment monitoring and disease tracking applications. In these applications, change detection depends on the ability to align the images of the sequence to a common axis, and the ability to build up memory about the image scene overtime. The process of aligning images to a common axis is termed image registration. The image scene representation is called site model. In this research we developed a novel registration technique to align temporal sequences of the same patient that will facilitate the construction of scene memory or site model with the ultimate goal of performing change detection. We have developed a statistical model supported approach for enhanced segmentation and extraction of suspicious mass areas from mammographic images. With an appropriate statistical description of various discriminate characteristics of both true and false candidates from the localized areas, an improved mass detection may be achieved in computer-aided diagnosis. In this study, one type of morphological operation is derived to enhance disease patterns of suspected masses by cleaning up unrelated background clutters, and a model-based image segmentation is performed to localize the suspected mass areas using stochastic relaxation labeling scheme.
Document Details
- Document Type
- Technical Report
- Publication Date
- Sep 01, 1999
- Accession Number
- ADA381158
Entities
People
- Yue-joseph Wang
Organizations
- The Catholic University of America