diagnosis of breast tumors with sonographic texture analysis using run-length matrix

Background: early detection and reliable diagnosis of breast cancer could lead to improved cure rates and reduce mortality and management costs. objectives: to explore the potential of texture analysis based on run-length matrix features for classifying benign and malignant breast tumors in ultrasound imaging. methods: a total of 70 breast tumors (38 benign and 32 malignant) have used in the proposed computer-aided diagnosis system. twenty run-length matrix features have extracted for texture analysis in three normalizations (default, 3sigma, and 1% - 99%). linear discriminant analysis and principal component analysis have employed to transform raw data to lower-dimensional spaces and increase discriminative power. the features have classified by the first nearest neighbor classifier. results: the features under 3sigma normalization have designed via linear discriminant analysis indicated high performance in classifying benign and malignant breast tumors with a sensitivity of 96.87%, specificity of 100%, accuracy of 98.57%, positive predictive value of 100%, and negative predictive value of 97.43%. the area under receiver operating characteristic curve was 0.992. conclusions: run-length matrix features had a high potential to characterize and could help radiologist to diagnosis breast tumors.