Multiscale superpixel method for segmentation of breast ultrasound. (October 2020)

• Record Type: Journal Article
• Authors: Ilesanmi, Ademola Enitan; Idowu, Oluwagbenga Paul; Makhanov, Stanislav S.
• Is Part Of: Computers in biology and medicine. Volume 125(2020)
• Publication Date: 2020-10
• Subjects: Breast ultrasound image -- Speckle noise -- Superpixel decomposition -- Graph cut -- Symmetry shape; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2020.103879
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: Background: In medical diagnostics, breast ultrasound is an inexpensive and flexible imaging modality. The segmentation of breast ultrasounds to identify tumour regions is a challenging and complex task. The major problems of effective tumour identification are speckle noise, artefacts and low contrast. The gold standard for segmentation is manual processing; however, manual segmentation is a cumbersome task. To address this problem, the automatic multiscale superpixel method for the segmentation of breast ultrasounds is proposed. Methods: The original breast ultrasound image was transformed into multiscaled images, and then, the multiscaled images were preprocessed. Next, a boundary efficient superpixel decomposition of the multiscaled images was created. Finally, the tumour region was generated by the boundary graph cut segmentation method. The proposed method was evaluated with 120 images from the Thammassat University Hospital database. The dataset consists of 30 malignant, 30 benign tumors, 60 fibroadenoma, and 60 cyst images. Popular metrics, such as the accuracy, sensitivity, specificity, Dice index, Jaccard index and Hausdorff distance, were used for the evaluation. Results: The results indicate that the proposed method achieves segmentation accuracy of 97.3% for benign tumors, 94.2% for malignant, 96.4% for cysts and 96.7% for fibroadenomas. The results validate that the proposed model outperforms selected state-of-the-art segmentation methods. Conclusions: The proposed method outperforms selected state-of-the-art segmentation methods with an average segmentation accuracy of 94%.