Computational Bioinformatics & Bio-imaging Laboratory (CBIL)


 
 

Internal Use

 
Improving Clinical Diagnosis by Change Detection in Image Sequences (DAMD17-98-8045, DAMD17-98-8044)
 
 

Due to the highly variable behavior of cancerous masses, mammographic examination of breast cancer requires an accurate comparison of mammograms taken over a period of time and emphasizes on the change of breast tissue associated with cancer development. Since the lack of quantitative change detection scheme for mammograms has caused too many call-backs and unnecessary biopsies, this work aims to develop an automatic change detection method to quantitatively extract the clinically important changes of suspicious lesions for improved detection of breast cancer. We will apply our extensive expertise in image registration, deformable tissue modeling, and image segmentation, to a series of mammograms so that change information can be highlighted and further analyzed by radiologist's closer clinical inspection and by our well-established computer-aided diagnosis (CAD) system.

We will build a site model for each individual patient for monitoring the breast tissue changes and extend our current research on image registration, soft tissue modeling, and image segmentation, to the early detection of breast cancer. Specific aims include: 1) registration and segmentation of deformable breast tissue structures across a series of mammograms; 2) construction of a site model of the mammogram for individual patients showing the locations of regions of interest and associated diagnostic information; 3) identification of clinically significant changes in both global and local mass areas within the breast; and 4) integration and evaluation of the developed techniques with existing CAD prototype. At the conclusion of this project, we anticipate achieving the following: 1) establish a reliable technique of monitoring breast tissue changes associated with cancerous masses; 2) deliver a CAD prototype that can incorporate tissue change information from additional mammograms; 3) evaluate the merit of combining change detection and CAD for improved clinical diagnosis using multiple mammograms; and 4) acquire the experience necessary to explore multimodality imaging for unified detection, diagnosis and treatment assessment of breast cancer.

 

 

 

Back

 

 

 

Copyright ©2004, Computational Bioinformatics and Bioimaging Laboratory (CBIL), Advanced Research Institute, Virginia Tech.

Last Updated: 03/03/2009. Suggestions/Comments - Webmaster