Adjusted Quick Shift Phase Preserving Dynamic Range Compression method for breast lesions segmentation. (2020)

• Record Type: Journal Article
• Authors: Osman, Fatima M.; Yap, Moi Hoon
• Is Part Of: Informatics in medicine unlocked. Volume 20(2020)
• Publication Date: 2020
• Subjects: Breast ultrasound -- Lesion segmentation -- Breast cancer -- Fully convolutional networks -- Transfer learning -- Deep learning -- Conventional segmentation; Medical informatics -- Periodicals; 610.285
• DOI: 10.1016/j.imu.2020.100344
• Languages: English
• ISSNs: 2352-9148

Abstract

Abstract: Ultrasound imaging systems produce images that are affected by speckle. This is mainly due to the interference of the returning wave at the transducer aperture that degrades images quality. As the filtering stage affects the quality of segmentation for breast ultrasound images, significant research recently concentrated on eliminating the speckle using different mathematical methods. In this paper, we propose a new approach for segmentation of breast ultrasound images called Adjusted Quick Shift Phase Preserving Dynamic Range Compression (AQS-APPDRC). AQS-APPDRC consists of three steps: a preprocessing step by applying APPDRC Filter and Frost Filter, followed by proposed Adjusted Quick Shift segmentation for superpixel extraction, and a post processing step of Binary Thresholding for blob selection. The results of our proposed AQS-APPDRC segmentation is compared with two other conventional segmentation methods namely: QS-FR, and QS-PPDRC. In addition, this study considers two state-of-the-art Convolutional Neural Networks (CNNs), i.e. U-Net and FCNs (FCN-AlexNet, FCN-32s and FCN-16s) for comparison. The segmentation results are evaluated on two small breast ultrasound datasets, where Dataset A has 306 images and Dataset B has 163 images. The proposed AQS-APPDRC approach achieved the best performance amongst two conventional methods and the CNNs in terms of Dice, Specificity, and MCC, when evaluated on Dataset A. For Dataset B, FCN-16s showed the best Dice, Specificity, and MCC, but the proposed AQS-APPDRC achieved comparable results. For Sensitivity, FCN-32s showed the best result for both datasets. Our results revealed that, for CNNs, the size of dataset is always the key indicator for its performance. The conventional methods produce comparable results on small datasets.