A fusion-based approach for breast ultrasound image classification using multiple-ROI texture and morphological analyses
type: Item
instance of: scholarly article
title: A fusion-based approach for breast ultrasound image classification using multiple-ROI texture and morphological analyses
zbMATH Open document ID: 1423.92144
DOI: 10.1155/2016/6740956
author: Mohammad I. Daoud
author: Tariq M. Bdair
author: Mahasen Al-Najar
author: Rami Alazrai
published in: Computational \& Mathematical Methods in Medicine
publication date: 2017-08-10T00:00:00Z
review text: Summary: Ultrasound imaging is commonly used for breast cancer diagnosis, but accurate interpretation of breast ultrasound (BUS) images is often challenging and operator-dependent. Computer-aided diagnosis (CAD) systems can be employed to provide the radiologists with a second opinion to improve the diagnosis accuracy. In this study, a new CAD system is developed to enable accurate BUS image classification. In particular, an improved texture analysis is introduced, in which the tumor is divided into a set of nonoverlapping regions of interest (ROIs). Each ROI is analyzed using gray-level cooccurrence matrix features and a support vector machine classifier to estimate its tumor class indicator. The tumor class indicators of all ROIs are combined using a voting mechanism to estimate the tumor class. In addition, morphological analysis is employed to classify the tumor. A probabilistic approach is used to fuse the classification results of the multiple-ROI texture analysis and morphological analysis. The proposed approach is applied to classify 110 BUS images that include 64 benign and 46 malignant tumors. The accuracy, specificity, and sensitivity obtained using the proposed approach are 98.2\%, 98.4\%, and 97.8\%, respectively. These results demonstrate that the proposed approach can effectively be used to differentiate benign and malignant tumors.
Mathematics Subject Classification ID: 92C55
zbMATH DE Number: 6759171
zbMATH Keywords: breast ultrasound image classification
zbMATH Keywords: multiple-ROI texture
zbMATH Keywords: morphological analyses
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2016/6740956
OpenAlex ID: W2564921842
Wikidata QID: Q37581302
cites work: Automated breast cancer detection and classification using ultrasound images: a survey
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cites work: Combining Pattern Classifiers
description: scientific article; zbMATH DE number 6759171