Automatic Segmentation of the Skull in MRI Sequences Using Level Set Method

Article Preview

Abstract:

Magnetic Resonance Imaging (MRI) has been widely used in clinical diagnose. Segmentation of these images obtained by MRI is a necessary procedure in medical image processing. In this paper, an improved level set algorithm was proposed to optimize the segmentation of MRI image sequences based on article [1]. Firstly, we add an area term and the edge indicator function to the total energy function for single image segmentation. Secondly, we presented a new method which uses the circumscribed polygon of the previous segmentation result as the initial contour of the next image to achieve automatic segmentation of image sequences. The algorithm was tested on MRI image sequences provided by Chuiyanliu Hospital, Chaoyang District of Beijing; the results have indicated that the proposed algorithm can effectively enhance the segmentation speed and quality of MRI sequences.

You might also be interested in these eBooks

Info:

Periodical:

Pages:

2370-2375

Citation:

Online since:

June 2011

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2011 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

[1] Li C, Kao CY, Gore JC, Ding Z: Minimization of region-scalable fitting energy for image segmentation. Image Processing, IEEE Transactions on 2008, 17(10): 1940-(1949).

DOI: 10.1109/tip.2008.2002304

Google Scholar

[2] Cremers D, Rousson M, Deriche R: A review of statistical approaches to level set segmentation: integrating color, texture, motion and shape. International journal of computer vision 2007, 72(2): 195-215.

DOI: 10.1007/s11263-006-8711-1

Google Scholar

[3] Codella NCF, Weinsaft JW, Cham MD, Janik M, Prince MR, Wang Y: Left Ventricle: Automated Segmentation by Using Myocardial Effusion Threshold Reduction and Intravoxel Computation at MR Imaging1. Radiology 2008, 248(3): 1004.

DOI: 10.1148/radiol.2482072016

Google Scholar

[4] Yu Q, Clausi DA: IRGS: Image segmentation using edge penalties and region growing. IEEE transactions on pattern analysis and machine intelligence 2008: 2126-2139.

DOI: 10.1109/tpami.2008.15

Google Scholar

[5] Wang H, Oliensis J: Generalizing edge detection to contour detection for image segmentation. Computer Vision and Image Understanding 2010, 114(7): 731-744.

DOI: 10.1016/j.cviu.2010.02.001

Google Scholar

[6] Wang Z, Yang M: A fast clustering algorithm in image segmentation. In: 2010: IEEE; 2010: V6.

Google Scholar

[7] Zhu X, Zhang P, Shao J, Cheng Y, Zhang Y, Bai J: A snake-based method for segmentation of intravascular ultrasound images and its in vivo validation. Ultrasonics (2010).

DOI: 10.1016/j.ultras.2010.08.001

Google Scholar

[8] Tsai A, Yezzi Jr A, Willsky AS: Curve evolution implementation of the Mumford-Shah functional for image segmentation, denoising, interpolation, and magnification. Image Processing, IEEE Transactions on 2002, 10(8): 1169-1186.

DOI: 10.1109/83.935033

Google Scholar