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Title: A hybrid edge-based segmentation approach for ultrasound medical images
Authors: Gupta D.
Anand, Radhey Shyam
Published in: Biomedical Signal Processing and Control
Abstract: Ultrasound imaging is one of the most widely used and the cheapest diagnostic tools of medical imaging modalities. In this paper, a hybrid approach for accurate segmentation of the ultrasound medical images is presented that utilizes both the features of kernel fuzzy clustering with spatial constraints and edge based active contour method using distance regularized level set (DRLS) function. The result obtained from the kernel fuzzy clustering is utilized not only to initialize the curve that spreads to identify the estimated region or object boundaries, but also helps to estimate the optimal parameters, which are responsible for controlling the level set evolution. The DRLS formulation also increase the processing speed by removing the need of re-initialization of the level set function. The performance of the proposed method is evaluated by conducting the several experiments on both the synthetic and real ultrasound images. Experimental results show that the proposed method improves the segmentation accuracy and also produces better results by successfully segmenting the object boundaries compared to others. © 2016 Elsevier Ltd
Citation: Biomedical Signal Processing and Control (2017), 31(): 116-126
Issue Date: 2017
Publisher: Elsevier Ltd
Keywords: Distance regularized level set
Edge based active contour
Kernel fuzzy C-means
ISSN: 17468094
Author Scopus IDs: 57202197339
Author Affiliations: Gupta, D., Department of Electronics and Communication Engineering, VNIT, Nagpur, 440010, India
Anand, R.S., Department of Electrical Engineering, IIT Roorkee, Roorkee, 247667, India
Corresponding Author: Gupta, D.; Department of Electronics and Communication Engineering, VNITIndia; email:
Appears in Collections:Journal Publications [EE]

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