A novel technique for detecting suspicious lesions in breast ultrasound images. (25th June 2015)

• Record Type: Journal Article
• Authors: Karimi, Behnam; Krzyżak, Adam
• Other Names: Olabarriaga Silvia Delgado guestEditor.; Wilkins‐Diehr Nancy guestEditor.; Smari Waleed W. guestEditor.; Bakhouya Mohamed guestEditor.; Fiore Sandro guestEditor.; Aloisio Giovanni guestEditor.
• Is Part Of: Concurrency and computation. Volume 28:Number 7(2016)
• Page Start: 2237
• Page End: 2260
• Publication Date: 2015-06-25
• Subjects: ultrasound images -- breast cancer detection -- image processing -- classification; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.3553
• Languages: English
• ISSNs: 1532-0626

Abstract

Summary: We present a new method for automatic detection of suspicious breast cancer lesions using ultrasound. The system is fully automated. It uses fuzzy logic and compounding for de‐noising. A fuzzy membership function based on the gray values of ultrasound images is applied for de‐noising, improving the quality of the image and increasing separation between foreground and background, thus making easier detection of lesions. A novel approach based on neural network is used for segmentation of ultrasound images, and correlation between ultrasound images taken from different angles allows overcoming the problem of shadowing. We consider a combination of morphological and texture features and use sequential forward search, sequential backward search, and distance‐based method to select the best subset of features. We rank the features using distance‐based method and use a combination of sequential forward search and sequential backward search to select the best features (bidirectional search). Finally, support vector machine classifier is used for detecting suspicious lesions. The results of experiments show that our system performs better than other state‐of‐the‐art computer‐aided diagnosis systems with the accuracy of 98.75%. Furthermore, we used concurrency to improve the computational efficiency. In concurrent implementation of de‐noising, segmentation, and feature selection and extraction, we assign each pixel of an ultrasound image to a different thread. We also benefit from multi‐core computing by running each classifier on a different thread. Concurrent implementation of our computer‐aided diagnosis system reduces overall computational time by 85%. Copyright © 2015 John Wiley & Sons, Ltd.