Steepest changes of a probability‐based cost function for delineation of mammographic masses: A validation study

Abstract

Our purpose in this work was to develop an automatic boundary detection method for mammographic masses and to rigorously test this method via statistical analysis. The segmentation method utilized a steepest change analysis technique for determining the mass boundaries based on a composed probability density cost function. Previous investigators have shown that this function can be utilized to determine the border of the mass body. We have further analyzed this method and have discovered that the steepest changes in this function can produce mass delineations that include extended projections. The method was tested on 124 digitized mammograms selected from the University of South Florida's Digital Database for Screening Mammography (DDSM). The segmentation results were validated using overlap, accuracy, sensitivity, and specificity statistics, where the gold standards were manual traces provided by two expert radiologists. We have concluded that the best intensity threshold corresponds to a particular steepest change location within the composed probability density function. We also found that our results are more closely correlated with one expert than with the second expert. These findings were verified via Analysis of Variance (ANOVA) testing. The ANOVA tests obtained p-values ranging from 1.03 x 10(-2)-7.51 x 10(-17) for the single observer studies and 2.03 x 10(-2)-9.43 x 10(-4) for the two observer studies. Results were categorized using three significance levels, i.e., p<0.001 (extremely significant), p <0.01 (very significant), and p <0.05 (significant), respectively.

Joint segmentation and spiculation detection for ill-defined and spiculated mammographic masses

“…Thus, the algorithm could accurately and robustly segmenting masses in the presence of overlapping or surrounding dense glandular tissues and chest borders. Traditional region growing based methods (e.g., [2]) may easily leak into these unwanted areas, if they are designed to include more mass margin portions.…”

“…Kupinski and Giger [1] proposed two region-growing approaches based on the radial gradient index and a probabilistic model. Kinnard et al [2] extended the probabilistic model based method by further analyzing the steepest change of the cost functions. Their method was found to be able to further include some of ill-defined mass boundaries.…”

Automatic categorization of mammographic masses using BI-RADS as a guidance

“…The integration of the automatic mass segmentation algorithm, e.g. [16], into the current system will be of great value in a clinical environment.…”

A new automatic mass detection method for breast cancer with false positive reduction

“…As stated above, MCL is a typical region growing with fixed grayscale increment. Several criterions have been proposed qto select good segmentation from a series of segmentations, such as RGI [26], steepest change of a probability-based cost function [27]. Here a simple procedure is used to choose the good one: (1) the area should be limited, (2) the area change should be small.…”