An automatic method of brain tumor segmentation from MRI volume based on the symmetry of brain and level set method

This paper presents a brain tumor segmentation method which automatically segments tumors from human brain MRI image volume. The presented model is based on the symmetry of human brain and level set method. Firstly, the midsagittal plane of an MRI volume is searched, the slices with potential tumor of the volume are checked out according to their symmetries, and an initial boundary of the tumor in the slice, in which the tumor is in the largest size, is determined meanwhile by watershed and morphological algorithms; Secondly, the level set method is applied to the initial boundary to drive the curve evolving and stopping to the appropriate tumor boundary; Lastly, the tumor boundary is projected one by one to its adjacent slices as initial boundaries through the volume for the whole tumor. The experiment results are compared with hand tracking of the expert and show relatively good accordance between both.

[1]  Tony F. Chan,et al.  Active contours without edges , 2001, IEEE Trans. Image Process..

[2]  J. Sethian,et al.  Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations , 1988 .

[3]  Qingmin Liao,et al.  Statistical Structure Analysis in MRI Brain Tumor Segmentation , 2007, Fourth International Conference on Image and Graphics (ICIG 2007).

[4]  Kai-Kuang Ma,et al.  Tumor segmentation from magnetic resonance imaging by learning via one-class support vector machine , 2004 .

[5]  Demetri Terzopoulos,et al.  Snakes: Active contour models , 2004, International Journal of Computer Vision.

[6]  Guido Gerig,et al.  Level-set evolution with region competition: automatic 3-D segmentation of brain tumors , 2002, Object recognition supported by user interaction for service robots.

[7]  Baba C. Vemuri,et al.  Topology-independent shape modeling scheme , 1993, Optics & Photonics.

[8]  Christine Fernandez-Maloigne,et al.  Evidential segmentation scheme of multi-echo MR images for the detection of brain tumors using neighborhood information , 2004, Inf. Fusion.

[9]  D. Mumford,et al.  Optimal approximations by piecewise smooth functions and associated variational problems , 1989 .

[10]  Mark W. Schmidt,et al.  3D Variational Brain Tumor Segmentation using a High Dimensional Feature Set , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[11]  Barry T. Thomas,et al.  Using Neural Networks to Automatically Detect Brain Tumours in MR Images , 1997, Int. J. Neural Syst..

[12]  Matei Mancas,et al.  Iterative Watersheds and Fuzzy Tumor Visualization , 2003 .

[13]  F. Kruggel,et al.  Segmentation of white matter lesions fromvolumetri , 2022 .

[14]  F. K. Lam,et al.  Semi-automatic tumor boundary detection in MR image sequences , 2001, Proceedings of 2001 International Symposium on Intelligent Multimedia, Video and Speech Processing. ISIMP 2001 (IEEE Cat. No.01EX489).

[15]  Frithjof Kruggel,et al.  Segmentation of Focal Brain Lesions , 2004, MIAR.

[16]  Jean-Marc Constans,et al.  Fuzzy Information Fusion Scheme Used to Segment Brain Tumor from MR Images , 2003, WILF.

[17]  Baba C. Vemuri,et al.  Shape Modeling with Front Propagation: A Level Set Approach , 1995, IEEE Trans. Pattern Anal. Mach. Intell..