Abstract—Brain tumor, a mass of tissue that grows out of control is one of the major causes for the increase in mortality among children and adults. Segmenting the regions of brain is the major challenge in tumor detection. A large number of effective segmentation algorithms have been used for segmentation in grey scale images ranging from simple edge-based methods to composite high-level approaches using modern and advanced pattern recognition approaches. Gradient vector field is an effective methodology applied to extract objects from complex backgrounds. The methodology has been effectively applied to extract different types of cancer like breast, skin, stomach etc. This paper uses a segmentation methodology called Gradient Vector Field, which uses energy as the feature to segment brain tumor along with a number of standard object detection algorithms mainly Sobel, Canny, Roberts, Prewitt and Laplacian. The performance of all the algorithms is tested on synthetic datasets followed by real MRI images. This paper (i) concludes the superiority of a particular methodology over others (ii) explains in detail the runtime analysis of the algorithms (iii) In depth analysis of the manual calculations of the parameters related to all the algorithms resulting into an optimized result with minimum error.
Index Terms—Tumor detection, gradient vector flow (GVF), active contour flow, sobel, canny, roberts, prewitt and laplacian.
A. Singh, S. Karanam, A. Choubey, and T. Raviteja are with the National Institute of Technology, Warangal, 506004 (e-mail: amarjotsingh@ieee.org).
S. Bajpai is with the Indian School of Mines Dhanbad, Jharkhand, India.
Cite: Amarjot Singh, Shivesh Bajpai, Srikrishna Karanam, Akash Choubey, and Thaluru Raviteja, "Malignant Brain Tumor Detection," International Journal of Computer Theory and Engineering vol. 4, no. 6, pp. 1002-1006, 2012.
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