An automated confirmatory system for analysis of mammograms. Issue 125 (March 2016)

• Record Type: Journal Article
• Authors: Peng, W.; Mayorga, R.V.; Hussein, E.M.A.
• Is Part Of: Computer methods and programs in biomedicine. Issue 125(2016)
• Page Start: 134
• Page End: 144
• Publication Date: 2016-03
• Subjects: Computer-aided diagnosis -- Mammogram -- Breast-cancer -- Texture-feature -- Rough-set theory -- Artificial Neural Networks; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2015.09.019
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: This paper presents an integrated system for the automatic analysis of mammograms to assist radiologists in confirming their diagnosis in mammography screening. The proposed system consists of four off-line stages: Image pre-processing and segmentation Feature extraction Selection of fundamental features using Rough Set theory Training of an Artificial Neural Network. Once the system is trained and tuned, is ready for its on-line use. Here the system is successfully tested on two independent databases. Abstract: This paper presents an integrated system for the automatic analysis of mammograms to assist radiologists in confirming their diagnosis in mammography screening. The proposed automated confirmatory system (ACS) can process a digitalized mammogram online, and generates a high quality filtered segmentation of an image for biological interpretation and a texture-feature based diagnosis. We use a serial of image pre-processing and segmentation techniques, including 2D median filtering, seeded region growing (SRG) algorithm, image contrast enhancement, to remove noise, delete radiopaque artifacts and eliminate the projection of the pectoral muscle from a digitalized mammogram. We also develop an entire-image texture-feature based classification method, by combining a Rough-set approach to extract five fundamental texture features from images, and then an Artificial Neural Network technique to classify a mammogram as: normal; indicating the presence of a benign lump; or representing a malignant tumor. Here, 222 random images from the Mammographic Image Analysis Society (MIAS) database are used for the offline ACS training. Once the system is tuned and trained, it is ready for the automated use for the analysis and diagnosis of new mammograms. To test the trained system, a separate set of 100 random images from the MIAS and another set of 100 random images from the independent BancoWeb database are selected. The proposed ACS is shown to be successful in confirming diagnosis of mammograms from the two independent databases.