Local energy-based shape histogram feature extraction technique for breast cancer diagnosis

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. https://doi.org/10.1016/j.eswa.2015.04.057

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.

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

Journal
Expert Systems with Applications: Volume 42, Issue 20

• Elsevier-journal-Expert-Systems-Applications-2015-published-LESH-Classification-paper-final.pdf