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

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

An optimized non-local means filter using automated clustering based preclassification through gap statistics for speckle reduction in breast ultrasound images

Speckle noise is a characteristic artifact in breast ultrasound images, which hinders substantive information essential for clinical diagnosis. In this article, we have investigated the use of Non-local means (NLM) filter, which is robust against severe noise, to remove speckle noise in breast ultrasound images. Medical diagnosis systems cannot employ traditional NLM filters, which exhibit the slowest performance due to their computational burden during the weighted averaging process. We have integrated a novel automated clustering based preclassification scheme using spatial regularized fuzzy c means (FCM) to alleviate the process. The appropriate number of clusters for each image is calculated automatically through Gap statistics. Moreover, the rotationally invariant moment distance measure increases the chance of getting more similar regions for NLM process. The algorithm is evaluated on a breast ultrasound database, which consists of 54 images including 28 benign and 26 malignant. Two statistical measures, Pratt’s figure of merit (PFM) and equivalent number of looks (ENL), are used to evaluate the noise suppression performance as well as the capability of preserving the fine details. The results of the proposed method are compared with the other three state of the art methods quantitatively. The proposed method demonstrated excellent despeckling performance with PFM of 0.91 and ENL of 7.415. The robustness against speckle noise and the acceptable processing time make the method more appropriate for computer aided diagnosis systems