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

• Local energy-based shape histogram features for abnormality detection (mammograms).
• Experimentation on INbreast and MIAS datasets.
• Study impact of selecting subset of the features upon classification performance.
• Achieved a higher classification accuracy of 100.00% with the SVM linear.

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.