Tree-structured nonlinear filter and wavelet transform for microcalcification segmentation in mammography

Publisher Website
Google Scholar
Abstract
The development of an extensive array of algorithms for both image enhancement and feature extraction for microcalcification cluster detection is reported. Specific emphasis is placed on image detail preservation and automatic or operator independent methods to enhance the sensitivity and specificity of detection and that should allow standardization of breast screening procedures. Image enhancement methods include both novel tree structured non-linear filters with fixed parameters and adaptive order statistic filters designed to further improve detail preservation. Novel feature extraction methods developed include both two channel tree structured wavelet transform and three channel quadrature mirror filter banks with multiresolution decomposition and reconstruction specifically tailored to extract MCC's. These methods were evaluated using fifteen representative digitized mammograms where similar sensitivity (true positive (TP) detection rate 100%) and specificity (0.1 - 0.2 average false positive (FP) MCC's/image) was observed but with varying degrees of detail preservation important for characterization of MCC's. The image enhancement step proved to be very critical to minimize image noise and associated FP detection rates for MCC's or individual microcalcifications.

This publication has 0 references indexed in Scilit: