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Combined adaptive enhancement and region‐growing segmentation of breast masses on digitized mammograms
Author(s) -
Petrick Nicholas,
Chan HeangPing,
Sahiner Berkman,
Helvie Mark A.
Publication year - 1999
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.598658
Subject(s) - segmentation , artificial intelligence , mammography , region growing , computer science , computer vision , image segmentation , pattern recognition (psychology) , filter (signal processing) , data set , computer aided diagnosis , scale space segmentation , medicine , breast cancer , cancer
As an ongoing effort to develop a computer aid for detection of masses on mammograms, we recently designed an object‐based region‐growing technique to improve mass segmentation. This segmentation method utilizes the density‐weighted contrast enhancement (DWCE) filter as a preprocessing step. The DWCE filter adaptively enhances the contrast between the breast structures and the background. Object‐based region growing was then applied to each of the identified structures. The region‐growing technique uses gray‐scale and gradient information to adjust the initial object borders and to reduce merging between adjacent or overlapping structures. Each object is then classified as a breast mass or normal tissue based on extracted morphological and texture features. In this study we evaluated the sensitivity of this combined segmentation scheme and its ability to reduce false positive (FP) detections on a data set of 253 digitized mammograms, each of which contained a biopsy‐proven breast mass. It was found that the segmentation scheme detected 98% of the 253 biopsy‐proven breast masses in our data set. After final FP reduction, the detection resulted in 4.2 FP per image at a 90% true positive (TP) fraction and 2.0 FPs per image at an 80% TP fraction. The combined DWCE and object‐based region growing technique increased the initial detection sensitivity, reduced merging between neighboring structures, and reduced the number of FP detections in our automated breast mass detection scheme.