Collaborative Research Experiences for Women (CREW) Project at BU's CS Department

This project is sponsored by the Computing Research Association Committee on the Status of Women in Computing Research (CRA-W) in cooperation with the National Science Foundation. The program is designed to provide collaborative research experiences for groups of two to three undergraduate women during the academic year. It is hoped that by increasing the opportunity to do research and by decreasing the isolation that may be experienced in doing independent research, women scientists and engineers will be encouraged to pursue similar work in graduate school. More information about the program can be found at


Undergraduate Students Faculty Mentor
Sarah Dubauskas
Kenda Stewart
Margrit Betke

Project Overview

Data mining uses database queries to search for hidden patterns in data. Little work has been done in searching medical image databases for hidden patterns [Brodley et al 1999]. A large number of computed tomography (CT) scans are produced regularly to follow the 8.2 million patients with a history of cancer in the US. Lung cancer screening of smokers is still controversial. If accepted it would result in an explosion of the number of chest CT scans to be analyzed. Preliminary computer-aided diagnosis (CAD) systems have been developed that attempt to copy the rules that radiologists use in evaluating chest CT scans and detecting pulmonary nodules [Ko, Betke 2001]. However, a ``gold istandard'' for these rules has not been established. More sophisticated and advanced database and data mining systems may be able to optimally use the information and knowledge stored in CAD systems and potentially improve the diagnostic capabilities of radiologists. We plan to design indexing and data mining algorithms for a database of chest CT scans. Database searches will be based on spatial and temporal properties of nodules, such as location, shape, and volumetric changes in consecutive CT studies. Queries such as "Where are the majority of stable nodules located?" and "Find a patient with a nodule that has a similar growth pattern" would be run on the database. These queries may reveal information about the differences between malignant and benign nodules. Our long-term goal is to discover properties and characteristics that can be used to assist physicians in interpreting diagnostic imaging studies.

Axial CT Image of Chest
Initial Segmentation of Bone Structure

Relevant papers:

M. Betke and J. P. Ko, "Detection of Pulmonary Nodules on CT and Volumetric Assessment over Time." In C. Taylor and A. Colchester, editors, Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 245--252, Cambridge, UK, September 1999, Springer-Verlag, Berlin.

C. Brodley, A. Kak, C. Shyu, J. Dy, "Content-Based Retrieval from Medical Image Databases: A Synergy of Human Interaction, Machine Learning, and Computer Vision," Proceedings of the Sixteenth National Conference on Artificial Intelligence July 18-22, 1999, Orlando, FL, pp. 760-767

Margrit Betke, Assistant Professor
Computer Science Department
Boston University
111 Cummington Street ( campus map )
Phone: 617-353-6412 or 8919
Fax: 617-353-6457

Last updated: November 20, 2002