Tumour localisation in ultrasound‐guided high‐intensity focused ultrasound ablation using improved gradient and direction vector flow. Issue 10 (1st October 2015)

• Record Type: Journal Article
• Authors: Le, Yi; Xu, Xianze; Zha, Li; Zhao, Wencheng; Zhu, Yanyan
• Is Part Of: IET image processing. Volume 9:Issue 10(2015)
• Page Start: 857
• Page End: 865
• Publication Date: 2015-10-01
• Subjects: gradient methods -- biomedical ultrasonics -- ultrasonic imaging -- medical image processing -- image segmentation -- tumours -- diseases -- speckle -- image denoising -- computational complexity; tumour localisation -- ultrasound‐guided high‐intensity focused ultrasound ablation system -- HIFU ablation system -- US image segmentation method -- diseased tissues -- healthy tissues -- speckle noise -- gradient‐direction vector flow model -- G&DVF model -- vector field -- computational complexity; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2014.0439
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : As one of the key technologies in ultrasound (US)‐guided high‐intensity focused ultrasound (HIFU) ablation systems, a precise and automatic US image segmentation method for tumour localisation can contribute to ablation of the diseased tissues and avoiding unwanted destruction of the healthy tissues. Owing to the speckle noise and the irregular shapes of target tumours in US images, traditional image segmentation methods are not suitable for tumour localisation. In this study, the authors proposed an improved gradient and direction vector flow (G&DVF) model to segment US images for tumour localisation. The conventional G&DVF model was improved in three aspects to obtain a better segmentation for tumour localisation. The straight lines were changed into fold lines to increase their flexibility in guiding the active contour. Several weighting parameters were added to the energy functional to control the influences of these different lines. Moreover, a novel vector field was defined to reduce the computational complexity and save the computation time. With these three aspects improved, the improved G&DVF model was applied in US image segmentation for tumour localisation. The experimental results demonstrate that this proposed model is reliable, accurate and time saving in US image segmentation for tumour localisation.