Diagnosis of the Abdominal Aorta Aneurysm in Magnetic Resonance Imaging Images

Authors: W. Kultangwattana, K. Somkantha, P. Phuangsuwan

Abstract:

This paper presents a technique for diagnosis of the abdominal aorta aneurysm in magnetic resonance imaging (MRI) images. First, our technique is designed to segment the aorta image in MRI images. This is a required step to determine the volume of aorta image which is the important step for diagnosis of the abdominal aorta aneurysm. Our proposed technique can detect the volume of aorta in MRI images using a new external energy for snakes model. The new external energy for snakes model is calculated from Law-s texture. The new external energy can increase the capture range of snakes model efficiently more than the old external energy of snakes models. Second, our technique is designed to diagnose the abdominal aorta aneurysm by Bayesian classifier which is classification models based on statistical theory. The feature for data classification of abdominal aorta aneurysm was derived from the contour of aorta images which was a result from segmenting of our snakes model, i.e., area, perimeter and compactness. We also compare the proposed technique with the traditional snakes model. In our experiment results, 30 images are trained, 20 images are tested and compared with expert opinion. The experimental results show that our technique is able to provide more accurate results than 95%.

Keywords: Adbominal Aorta Aneurysm, Bayesian Classifier, Snakes Model, Texture Feature.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1076658

References:

[1] GR Gadowski, DB Pilcher and MA Ricci, "Abdominal Aortic Aneurysm Expansion Rate: Effect of Size and Beta-Adrenergic Bolckade, J Vasc Surg, 1994.
[2] J. Jan, Medical Image Processing Reconstruction and Restoration: Concepts and Methods, CRC Press, 2006.
[3] R.J. van Der Geest and J.H. Reiber, "Quantification in cardiac MRI," J.Magn. Reson. Imag., vol. 10, pp. 602-608, 1999.
[4] R.I. Pettigrew, J.N. Oshinski, G.Chatzimavroudis, and W.T. Dixon, "MRI techniques for cardiovascular imaging," J. Magn. Reson. Imag., vol. 10, pp. 590-601, 1999.
[5] J.R. Parker, Algorithms for Image Processing and Computer Vision, Wiley Computer Publishing, 1997.
[6] R.C. Gonzalez and R.E. Woods, Digital Image Processing. Reading, MA: Addison Wesley, reprint, 1992.
[7] J. M. S. Prewitt, "Object Enhancement and Extraction," Picture Processing and Psychopictorics," Proc. IEEE, vol. 59, pp.75-149, 1970.
[8] G.S. Robinson, "Edge Detection by Compass Gradient Masks," Compute. Graph. Image Processing., vol.6,pp. 492-501, 1977.
[9] E. Argyle, "Techniques for edge detection," Proc. IEEE,, pp. 285-287, 1970.
[10] M. Kass, A. Witkin, and D. terzopoulos. "Snakes: Active Contour Models," International Journal of Computer vision, vol 1:pp.321-331, 1987.
[11] V. Caselles, F. Catte, T. Coll, and F. Dibos. "A Geometric Model for Active Contours," Numer Math, vol. 66, pp.1-31. 1993.
[12] F. Leymarie and M.D. Levine, "Tracking Deformable Objects in the Plane Using an Active Contour Model," IEEE Trans. Pattern Anal. and Machine intell., vol. 15, pp. 617-634, 1993.
[13] K. Laws, Textured Image Segmentation, Ph.D. Dissertation, 1980.
[14] K. Laws, "Rapid texture identification," SPIE, vol. 238, pp. 376-380, 1980.
[15] S. Theodoridis and K. Koutroumbas, Pattern Recognition, Academic Press, 1999.
[16] S. Aeberhard, D. Coomans, and O. Devel, "Comparative Analysis of Statistical Pattern Recognition Methods in High dimensional Setting," Pattern Recognition, vol. 27, pp. 1065-1077, 1994.
[17] W. kultangwattana, P. Phuangsuwan and K. Somkantha, "To Improve the Efficiency of Active Contour Model for Apply to Medical Image Segmentation of Aorta Images," National Conference on Computer Information Technologies, pp. 28-33, 2009.