Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/23798
Appears in Collections:Computing Science and Mathematics Journal Articles
Peer Review Status: Refereed
Title: Local energy-based shape histogram feature extraction technique for breast cancer diagnosis
Authors: Wajid, Summrina
Hussain, Amir
Contact Email: ahu@cs.stir.ac.uk
Keywords: Computer-aided decision support system (CADSS)
Local energy-based shape histogram (LESH)
Support vector machine (SVM)
Local energy model
Receiver operating characteristic (ROC) curve
Issue Date: 15-Nov-2015
Publisher: Elsevier
Citation: Wajid S & Hussain A (2015) Local energy-based shape histogram feature extraction technique for breast cancer diagnosis, Expert Systems with Applications, 42 (20), pp. 6990-6999.
Abstract: This paper proposes a novel local energy-based shape histogram (LESH) as the feature set for recognition of abnormalities in mammograms. It investigates the implication of this technique on mammogram datasets of the Mammographic Image Analysis Society and INbreast. In the evaluation, regions of interest were extracted from the mammograms, their LESH features calculated, and fed to support vector machine (SVM) classifiers. In addition, the impact of selecting a subset of LESH features on classification performance was also observed and benchmarked against a state-of-the-art wavelet based feature extraction method. The proposed method achieved a higher classification accuracy of 99.00±0.50, as well as an Az value of 0.9900±0.0050 with multiple SVM kernels, where a linear kernel performed with 100% accuracy for distinguishing between the abnormalities (masses vs. microcalcifications). Hence, the general capability of the proposed method was established, in which it not only distinguishes between malignant and benign cases for any type of abnormality but also among different types of abnormalities. It is therefore concluded that LESH features are an excellent choice for extracting significant clinical information from mammogram images with significant potential for application to 3-D MRI images.
Type: Journal Article
URI: http://hdl.handle.net/1893/23798
DOI Link: http://dx.doi.org/10.1016/j.eswa.2015.04.057
Rights: This item has been embargoed for a period. During the embargo please use the Request a Copy feature at the foot of the Repository record to request a copy directly from the author. You can only request a copy if you wish to use this work for your own research or private study. Accepted refereed manuscript of: Wajid S & Hussain A (2015) Local energy-based shape histogram feature extraction technique for breast cancer diagnosis, Expert Systems with Applications, 42 (20), pp. 6990-6999. DOI: 10.1016/j.eswa.2015.04.057 © 2015, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/
Affiliation: University of Stirling
Computing Science - CSM Dept



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