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Appears in Collections:Computing Science and Mathematics Journal Articles
Peer Review Status: Refereed
Title: Three-Dimensional Local Energy-Based Shape Histogram (3D-LESH)-Based Feature Extraction - A Novel Technique
Author(s): Wajid, Summrina
Hussain, Amir
Huang, Kaizhu
Keywords: Clinical decision support system (CDSS)
Echo state network (ESN)
Extreme learning machine (ELM)
Local energy-based shape histogram (LESH)
Magnetic resonance imaging (MRI)
Support vector machine (SVM)
Issue Date: 1-Dec-2018
Citation: Wajid S, Hussain A & Huang K (2018) Three-Dimensional Local Energy-Based Shape Histogram (3D-LESH)-Based Feature Extraction - A Novel Technique. Expert Systems with Applications, 112, pp. 388-400.
Towards visually-driven speech enhancement for cognitively-inspired multi-modal hearing-aid devices
Abstract: In this paper, we present a novel feature extraction technique, termed Three-Dimensional Local Energy-Based Shape Histogram (3D-LESH), and exploit it to detect breast cancer in volumetric medical images. The technique is incorporated as part of an intelligent expert system that can aid medical practitioners making diagnostic decisions. Analysis of volumetric images, slice by slice, is cumbersome and inefficient. Hence, 3D-LESH is designed to compute a histogram-based feature set from a local energy map, calculated using a phase congruency (PC) measure of volumetric Magnetic Resonance Imaging (MRI) scans in 3D space. 3D-LESH features are invariant to contrast intensity variations within different slices of the MRI scan and are thus suitable for medical image analysis. The contribution of this article is manifold. First, we formulate a novel 3D-LESH feature extraction technique for 3D medical images to analyse volumetric images. Further, the proposed 3D-LESH algorithmis, for the first time, applied to medical MRI images. The final contribution is the design of an intelligent clinical decision support system (CDSS) as a multi-stage approach, combining novel 3D-LESH feature extraction with machine learning classifiers, to detect cancer from breast MRI scans. The proposed system applies contrast-limited adaptive histogram equalisation (CLAHE) to the MRI images before extracting 3D-LESH features. Furthermore, a selected subset of these features is fed into a machine-learning classifier, namely, a support vector machine (SVM), an extreme learning machine (ELM) or an echo state network (ESN) classifier, to detect abnormalities and distinguish between different stages of abnormality. We demonstrate the performance of the proposed technique by its application to benchmark breast cancer MRI images. The results indicate high-performance accuracy of the proposed system (98%±0.0050, with an area under a receiver operating charactertistic curve value of 0.9900 ± 0.0050) with multiple classifiers. When compared with the state-of-the-art wavelet-based feature extraction technique, statistical analysis provides conclusive evidence of the significance of our proposed 3D-LESH algorithm.
DOI Link: 10.1016/j.eswa.2017.11.057
Rights: This article is published under a Creative Commons Attribution 4.0 International license (CC BY 4.0):

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