Particle swarm optimization based fusion of ultrasound echographic and elastographic texture features for improved breast cancer detection.

Abstract

Breast cancer remains the main cause of cancer deaths among women in the world. As per the statistics, it is the most common killer disease of the new era. Since 2008, breast cancer incidences have increased by more than 20%, while mortality has increased by 14%. The statistics of breast cancer incidences as per GLOBOCAN project for the years 2008 and 2012 show an increase from 22.2 to 27% globally. In India, breast cancer accounts for 25% to 31% of all cancers in women. Mammography and Sonography are the two common imaging techniques used for the diagnosis and detection of breast cancer. Since Mammography fails to spot many cancers in the dense breast tissue of young patients, Sonography is preferred as an adjunct to Mammography to identify, characterize and localize breast lesions. This work aims to improve the performance of breast cancer detection by fusing the texture features from ultrasound elastographic and echographic images through Particle Swarm Optimization. The mean classification accuracy of Optimum Path Forest Classifier is used as an objective function in PSO. Seven performance metrics were computed to study the performance of the proposed technique using GLCM, GLDM, LAWs and LBP texture features through Support Vector Machine classifier. LBP feature provides accuracy, sensitivity, specificity, precision, F1 score, Mathews Correlation Coefficient and Balanced Classification Rate as 96.2%, 94.4%, 97.4%, 96.2%, 95.29%, 0.921, 95.88% respectively. The obtained performance using LBP feature is better compared to the other three features. An improvement of 6.18% in accuracy and 11.19% in specificity were achieved when compared to those obtained with previous works.