Robust Detection of Masses in Digitized Mammograms
Abstract
This project is to develop a robust computer aided diagnosis (CAD) system for mass detection with high sensitivity and specificity in digitized mammograms. The research scope in past year is to improve and optimize detection performance and classification generalizability. Several major progresses have been made including (1). A novel graph-based algorithm was proposed to segment stellate masses in mammograms by separating the adjacent regions while keeping the spiculation of masses. It is helpful for the improvement of stellate late mass and distortion detection. (2). A hybrid "hard"-"soft" classification method was proposed, where the "hard" decision classifier is cascaded with a "soft" decision classification with the objective to reduce false-positives (FPs) in the cases with multiple FPs retained after the "hard" decision classification. It has a much better performance and generalizability of classification. (3!. A training database was generated for fine tuning the parameters of CAD system. An FROC curve of CAD mass detection using training database was obtained. It is expected that these processing will be very helpful in improving the robustness of the detection system.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jun 01, 2002
- Accession Number
- ADA406818
Entities
People
- Lihua Li
Organizations
- University of South Florida