Paper
17 March 2008 Comparison of mammographic parenchymal patterns of normal subjects and breast cancer patients
Author Affiliations +
Abstract
In this study, we compared the texture features of mammographic parenchymal patterns (MPPs) of normal subjects and breast cancer patients and evaluated whether a texture classifier can differentiate their MPPs. The breast image was first segmented from the surrounding image background by boundary detection. Regions of interest (ROIs) were extracted from the segmented breast area in the retroareolar region on the cranio-caudal (CC) view mammograms. A mass set (MS) of ROIs was extracted from the mammograms with cancer, but ROIs overlapping with the mass were excluded. A contralateral set (CS) of ROIs was extracted from the contralateral mammograms. A normal set (NS) of ROIs was extracted from one CC view mammogram of the normal subjects. Each data set was randomly separated into two independent subsets for 2-fold cross-validation training and testing. Texture features from run-length statistics (RLS) and newly developed region-size statistics (RSS) were extracted to characterize the MPP of the breast. Linear discriminant analysis (LDA) was performed to compare the MPP difference in each of the three pairs: MS-vs-NS, CS-vs-NS, and MS-vs-CS. The Az values for the three pairs were 0.79, 0.73, and 0.56, respectively. These results indicate that the MPPs of the contralateral breast of breast cancer patients exhibit textures comparable to that of the affected breast and that the MPPs of cancer patients are different from those of normal subjects.
© (2008) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Yi-Ta Wu, Berkman Sahiner, Heang-Ping Chan, Jun Wei, Lubomir M. Hadjiiski, Mark A. Helvie, Yiheng Zhang, Jiazheng Shi, Chuan Zhou, Jun Ge, and Jing Cui "Comparison of mammographic parenchymal patterns of normal subjects and breast cancer patients", Proc. SPIE 6915, Medical Imaging 2008: Computer-Aided Diagnosis, 691520 (17 March 2008); https://doi.org/10.1117/12.771278
Lens.org Logo
CITATIONS
Cited by 5 scholarly publications.
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Breast

Mammography

Breast cancer

Cancer

Image segmentation

Feature extraction

Received signal strength

Back to Top