Multiscale hybrid algorithm for pre-processing of ultrasound images

Abstract

BackgroundOne of the diagnostic tools for breast cancer screening is the ultrasound. Ultrasounds are characterized by low cost and non-invasive patterns. A major drawback of ultrasound is the multiplicative speckle noise. Speckle noise limits the effectiveness of images thereby reducing the efficiency of the test. This paper proposes a new algorithm to reduce noise. MethodsThe method is based on the combination of the multiscale approach, the wiener filter, and the new fast bilateral filter. A multiscale image was first created. Subsequently, the wiener filter was used for initial filtering and to reduce the mean square error of the multiscaled images. Finally, the new fast bilateral filter was used to remove speckle noise. ResultsThe algorithm was tested on 50 synthetic images degraded by speckle noise with varying intensity and 250 breast ultrasound (BUS) images from two datasets. The results were compared with selected state-of-the-art filters. The proposed approach shows better performance in terms of standard noise evaluation measures. The noise reduction in the US images of breast cancer (BUS) has been verified by selected conventional segmentation procedures. Results indicate that the proposed method reduced speckle better than other methods. Specifically, the proposed method produced a structural similarity index measure value of 95% for both benign and malignant tumors. In addition a peak signal-to-noise ratio of 30.41db and 30.75db for benign and malignant tumors were obtained. The proposed filter also achieves high accuracy in terms of the segmentation measures. ConclusionsThe proposed speckle noise reduction algorithm achieves better accuracies than other competing filters. The algorithm will act as a tool for speckle reduction in breast ultrasound images.