Classification of breast lesions in ultrasonography using sparse logistic regression and morphology-based texture features

Nemat, H., Fehri, H., Ahmadinejad, N., Frangi, A.F. and Gooya, A. (2018) Classification of breast lesions in ultrasonography using sparse logistic regression and morphology-based texture features. Medical Physics, 45(9), pp. 4112-4124. (doi: 10.1002/mp.13082) (PMID:29974971)

Abstract

Purpose: This work proposes a new reliable computer-aided diagnostic (CAD) system for the diagnosis of breast cancer from breast ultrasound (BUS) images. The system can be useful to reduce the number of biopsies and pathological tests, which are invasive, costly, and often unnecessary. Methods: The proposed CAD system classifies breast tumors into benign and malignant classes using morphological and textural features extracted from breast ultrasound (BUS) images. The images are first preprocessed to enhance the edges and filter the speckles. The tumor is then segmented semiautomatically using the watershed method. Having the tumor contour, a set of 855 features including 21 shape-based, 810 contour-based, and 24 textural features are extracted from each tumor. Then, a Bayesian Automatic Relevance Detection (ARD) mechanism is used for computing the discrimination power of different features and dimensionality reduction. Finally, a logistic regression classifier computed the posterior probabilities of malignant vs benign tumors using the reduced set of features. Results: A dataset of 104 BUS images of breast tumors, including 72 benign and 32 malignant tumors, was used for evaluation using an eightfold cross-validation. The algorithm outperformed six state-of-the-art methods for BUS image classification with large margins by achieving 97.12% accuracy, 93.75% sensitivity, and 98.61% specificity rates. Conclusions: Using ARD, the proposed CAD system selects five new features for breast tumor classification and outperforms state-of-the-art, making a reliable and complementary tool to help clinicians diagnose breast cancer.

• Item Type: Articles
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Gooya, Dr Ali
• Authors: Nemat, H., Fehri, H., Ahmadinejad, N., Frangi, A.F., and Gooya, A.
• College/School: College of Science and Engineering > School of Computing Science
• Journal Name: Medical Physics
• Publisher: Wiley
• ISSN: 0094-2405
• ISSN (Online): 2473-4209
• Published Online: 05 July 2018