Computer-aided Diagnosis Of Mammograms Research Articles

Breast cancer screening with adjunctive ultrasound mammography

An adjunctive ultrasound mammography system and associated methods are described, comprising a scanning apparatus for facilitating standardized, repeatable breast ultrasound scans, and further comprising an adjunctive ultrasound display apparatus configured for fast, intuitive viewing of adjunctive ultrasound data concurrently with x-ray mammogram information. In one preferred embodiment, thick-slice ultrasound images are displayed near an x-ray mammogram such that a screening radiologist can quickly view the thick-slice images for assistance in interpreting the x-ray mammogram. Methods for concurrently acquiring and displaying vibrational resonance image (VDI) data are described. Computer-aided diagnosis (CAD) algorithms that incorporate acoustically-based feature vectors are described, the feature vectors including lateral shadow metrics, vertical shadow metrics, posterior enhancement metrics, and VDI-based metrics. Algorithms are also described that combine x-ray mammogram CAD results with adjunctive ultrasound CAD results without requiring complex image registrations therebetween. Many of the practical, economic, and political barriers to acceptance and integration of ultrasound mammography into existing mass breast cancer screening environments are mitigated.

Usefulness of three-dimensional fractal analysis for breast cancer detection in computer-aided diagnosis of mammograms

This paper introduces a number of reliability criteria for computer-aided diagnostic systems for breast cancer. These criteria are then used to analyze some published neural network systems. It is also shown that the property of monotonicity for the data is rather natural in this medical domain, and it has the potential to significantly improve the reliability of breast cancer diagnosis while maintaining a general representation power. A central part of this paper is devoted to the representation/narrow vicinity hypothesis, upon which existing computer-aided diagnostic methods heavily rely. The paper also develops a framework for determining the validity of this hypothesis. The same framework can be used to construct a diagnostic procedure with improved reliability.