NDLI: Segmentation of brain tumors in MRI images using multi-scale gradient vector flow

Content Provider	IEEE Xplore Digital Library
Author	Kazerooni, A.F. Ahmadian, A. Serej, N.D. Rad, H.S. Saberi, H. Yousefi, H. Farnia, P.
Copyright Year	2011
Description	Author affiliation: Department of Biomedical Systems & Medical Physics, Tehran University of Medical Sciences (TUMS), and Image Guided Surgery Lab, Research Center for Science and technology in Medicine (RCSTIM) Tehran, Iran (Serej, N.D.; Rad, H.S.; Yousefi, H.; Farnia, P.) \|\| Department of Biomedical Systems & Medical Physics, Tehran University of Medical Sciences, and Image Guided Surgery Lab, Research Center for Science and technology in Medicine (RCSTIM), Tehran, Iran (Kazerooni, A.F.; Ahmadian, A.) \|\| Department of Neurosurgery, Imam Khomeini Hospital, Tehran University of Medical Sciences, and Brain and Spinal Injuries Repair Research Center (BASIR) (Saberi, H.)
Abstract	The gradient vector flow (GVF) algorithm has been used extensively as an efficient method for medical image segmentation. This algorithm suffers from poor robustness against noise as well as lack of convergence in small scale details and concavities. As a cure to this problem, in this paper the idea of multi scale is applied to the traditional GVF algorithm for segmentation of brain tumors in MRI images. Using this idea, the active contour is evolved with respect to scaled edge maps in a multi scale manner. The edge detection performance of the modified GVF algorithm is further enhanced by applying a threshold-based edge detector to improve the edge map. The Bspline snake is selected for representation of the active contour, due to its ability to capture corners and its local control. The results showed an improvement of 30% in the accuracy of tumor segmentation against traditional GVF and 10 % as compared to Bspline GVF in the presence of noise, besides the repeatability of the algorithm in contrast to traditional GVF. The clinical evaluation also proved the accuracy and sensitivity of the proposed method as 92.8% and 95.4%, respectively.
Starting Page	7973
Ending Page	7976
File Size	729067
Page Count	4
File Format	PDF
ISBN	9781424441211
ISSN	1557170X
e-ISBN	9781457715891
e-ISBN	9781424441228
DOI	10.1109/IEMBS.2011.6091966
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-08-30
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Tumors Image edge detection Image segmentation Noise Biomedical imaging Vectors Active contours B-spline snake Brain tumor segmentation Multi scale GVF traditional GVF
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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