Title: Improved fuzzy clustering and Legendre level sets for segmentation of multiple tumours in low contrast liver CTA images
Authors: V.M. Selvalakshmi; S. Nirmala Devi
Addresses: Department of ECE, College of Engineering, Anna University, Chennai 600025, India ' Department of ECE, College of Engineering, Anna University, Chennai 600025, India
Abstract: Accurate and fast image segmentation algorithms are of great importance in medical image processing. In this paper, a new, rapid and efficient region-based segmentation method for Liver tumour segmentation initialised using spatial FCM clustering technique is proposed. In Legendre level sets, the area of interest illumination is represented in lower dimensional subspace. A set of predefined basis functions such as Legendre basis function is used to represent the lower dimensional subspace. This kind of representation enables the robust segmentation of heterogeneous objects even in the presence of noise. The proposed algorithm has been compared with other existing algorithms and its performance evaluation is carried out in CTA abdomen images of various patients. The obtained results prove its effectiveness in low contrast inhomogeneous tumour segmentation.
Keywords: active contours; spatial FCM clustering; level set evolution; Legendre polynomials.
International Journal of Biomedical Engineering and Technology, 2017 Vol.25 No.2/3/4, pp.167 - 181
Available online: 23 Oct 2017 *Full-text access for editors Access for subscribers Purchase this article Comment on this article