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Please use this identifier to cite or link to this item: http://repository.iitr.ac.in/handle/123456789/6982
Title: A novel content-based active contour model for brain tumor segmentation
Authors: Sachdeva J.
Kumar V.
Gupta I.
Khandelwal N.
Ahuja C.K.
Published in: Magnetic Resonance Imaging
Abstract: Brain tumor segmentation is a crucial step in surgical and treatment planning. Intensity-based active contour models such as gradient vector flow (GVF), magneto static active contour (MAC) and fluid vector flow (FVF) have been proposed to segment homogeneous objects/tumors in medical images. In this study, extensive experiments are done to analyze the performance of intensity-based techniques for homogeneous tumors on brain magnetic resonance (MR) images. The analysis shows that the state-of-art methods fail to segment homogeneous tumors against similar background or when these tumors show partial diversity toward the background. They also have preconvergence problem in case of false edges/saddle points. However, the presence of weak edges and diffused edges (due to edema around the tumor) leads to oversegmentation by intensity-based techniques. Therefore, the proposed method content-based active contour (CBAC) uses both intensity and texture information present within the active contour to overcome above-stated problems capturing large range in an image. It also proposes a novel use of Gray-Level Co-occurrence Matrix to define texture space for tumor segmentation. The effectiveness of this method is tested on two different real data sets (55 patients - more than 600 images) containing five different types of homogeneous, heterogeneous, diffused tumors and synthetic images (non-MR benchmark images). Remarkable results are obtained in segmenting homogeneous tumors of uniform intensity, complex content heterogeneous, diffused tumors on MR images (T1-weighted, postcontrast T1-weighted and T2-weighted) and synthetic images (non-MR benchmark images of varying intensity, texture, noise content and false edges). Further, tumor volume is efficiently extracted from 2-dimensional slices and is named as 2.5-dimensional segmentation. © 2012 Elsevier Inc.
Citation: Magnetic Resonance Imaging (2012), 30(5): 694-715
URI: https://doi.org/10.1016/j.mri.2012.01.006
http://repository.iitr.ac.in/handle/123456789/6982
Issue Date: 2012
Keywords: 2.5-dimensional segmentation
Brain tumor segmentation
Content-based active contour (CBAC)
Heterogeneous tumors
Homogeneous tumors
Synthetic images
ISSN: 0730725X
Author Scopus IDs: 54999212000
25646515800
56211916300
7006950739
23048455900
Author Affiliations: Sachdeva, J., Department of Electrical Engineering, Indian Institute of Technology Roorkee, Roorkee, India
Kumar, V., Department of Electrical Engineering, Indian Institute of Technology Roorkee, Roorkee, India
Gupta, I., Department of Electrical Engineering, Indian Institute of Technology Roorkee, Roorkee, India
Khandelwal, N., Department of Radio Diagnosis, Post Graduate Institute of Medical Education and Research, Chandigarh, India
Ahuja, C.K., Department of Radio Diagnosis, Post Graduate Institute of Medical Education and Research, Chandigarh, India
Corresponding Author: Sachdeva, J.; Department of Electrical Engineering, Indian Institute of Technology Roorkee, Roorkee, India; email: jainysachdeva@gmail.com
Appears in Collections:Journal Publications [EE]

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