Hybrid segmentation method based on multi‐scale Gaussian kernel fuzzy clustering with spatial bias correction and region‐scalable fitting for breast US images. Issue 8 (5th November 2018)

• Record Type: Journal Article
• Authors: Panigrahi, Lipismita; Verma, Kesari; Singh, Bikesh Kumar
• Is Part Of: IET computer vision. Volume 12:Issue 8(2018)
• Page Start: 1067
• Page End: 1077
• Publication Date: 2018-11-05
• Subjects: Gaussian processes -- tumours -- pattern clustering -- biomedical ultrasonics -- medical image processing -- fuzzy set theory -- image segmentation -- speckle; hybrid segmentation method -- spatial bias correction -- speckle noise -- shadowing effects -- acoustic enhancement -- multiscale Gaussian kernel-induced FCM -- active contour -- region-scalable fitting energy function -- estimated regions -- breast ultrasound images -- image quality -- breast US images -- automated tumour segmentation -- multiscale Gaussian kernel-induced fuzzy C-means clustering method -- MsGKFCM_S clustering method -- controlling parameter estimation -- curve evolution process -- Jaccard index -- dice similarity -- shape similarity -- Hausdroff difference -- F-measure -- lesion detection; Computer vision -- Periodicals; Pattern recognition systems -- Periodicals; 006.37
• DOI: 10.1049/iet-cvi.2018.5332
• Languages: English
• ISSNs: 1751-9632

Abstract

Abstract : Automated segmentation of tumors in breast ultrasound (US) images is challenging due to poor image quality, presence of speckle noise, shadowing effects and acoustic enhancement. This paper improves the multi‐scale Gaussian kernel induced fuzzy C‐means clustering method with spatial bias correction (MsGKFCM_S). Furthermore, it presents a hybrid segmentation method, using both the features of the MsGKFCM_S clustering and active contour driven by a region‐scalable fitting energy function. The result obtained from the MsGKFCM_S method is utilised to initialise the contour that spreads to identify the estimated regions. It also helps to estimate the several controlling parameters of the curve evolution process. The proposed approach is evaluated on a database of 127 breast US images consisting of 75 malignant and 52 solid benign cases. The performance of proposed approach is compared with other related techniques, using performance measures such as Jaccard Index, dice similarity, shape similarity, Hausdroff difference, area difference, accuracy and F‐measure. Results indicate that the proposed approach can successfully detect lesions in breast US images, with high accuracy of 97.889 and 97.513%. Moreover, the proposed approach has the capability of handling shadowing effects, acoustic enhancement and multiple lesions.