The purpose of abnormality extraction is to understand and identify the abnormality. It is very helpful to the physician to gain knowledge of the severity of the disease. Region based segmentation is used for this purpose. In abnormality extraction, the threshold detection is very important and it can be consider as the key factor of the Intelligent Diagnostic Imaging System. Image segmentation is to partition an image into meaningful regions with respect to a particular application. The segmentation is based on measurements taken from the image and might be grey level, colour, texture, depth or motion. Usually image segmentation is an initial and vital step in a series of processes aimed at overall image understanding. This paper describes the algorithm for threshold detection for abnormality extraction. Different algorithms are used for T1 and T2 MRI Images. This paper also explains the comparison of different segmentation techniques. MATLAB tools are used to do the segmentation. The intensity level of a particular abnormality is same in an MRI image slice. It can be iso-intense, hypo-intense or hyper-intense.

1. Scott W. Atlas, “ Magnetic Resonance Imaging of the Brain and Spine (2 Volume Set)”.
2. Dr. Joseph P Hernak. “The basics of MRI”.
3. Anne G Osborn, Published by Amirsys(tm) , “Diagnostic Imaging: Brain”.
4. Peter H. Abrahams, Jonathan D. Spratt, Lonie R Salkowski, “Imaging Atlas of Human Anatomy”.
5. Jeff Ross, Michael Brant-Zawadzki, Mark Chen, Kevin Moore, Karen Salzman, “Diagnostic Imaging: Head &Neck”.
6. Anne G. Osborn , “Diagnostic Neuroradiology” .
7. Ric Harnsberger, Patricia Hudgins, Richard Wiggins, Christian Davidson , “Diagnostic Imaging: Head and Neck”.
8. Rafael C. Gonzalez, Richard E. Woods, Steven L. Eddins: “Digital Image Processing Using MATLAB : 378-425, 2005.
9. Rafael C. Gonzalez, Richard E. Woods: “Digital Image Processing 2: 567-638, 2002.
10. Russ, J. C. . The Image Processing Handbook, 3rd ed., CRC Press, Boca Raton, FL.
11. Natick, MA “Using MATLAB, Version 6.5 ,The MathWorks, Inc”.,.
12. I. N. Manousakas, P.E Undrill, G.G. Gameron and T.W.Redpath, “Split-and-Merge Segmentation of Magnetic Resonance Medical Images: Performance Evaluation and Extension to Three Dimensions”, Computers and Biomedical Research, Article No.CO981489, 0010-4809/98, pp.393-412, 1998.
13. Vincent, L. and Soille, P. . "Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations, " IEEE Transactions on Pattern Anal Machine Intell, vol. 13, no. 6, pp. 583-598.
14. Canny, J. . "A Computational Approach for Edge Detection," IEEE Transactions on Pattern Anal. Machine Intell, vol. 8, No. 6, pp. 679-698.
15. Sze, T. W. and Yang, Y. H. . " A Simple Contour Matching Algorithm." IEEE Transactions on Pattern Anal. Machine IntelL, vol. PAMI-3, no. 6, pp. 676-678.
16. Vincent, L. , "Morphological Grayscale Reconstruction in Image Analysis: Applications and Efficient Algorithms," IEEE Transactions on Image Processing vol. 2, no. 2, pp. 176-201.
17. Soille, P. . Morphological Image Analysis: Principles and Applications. 2nd ed.. Springer-Verlag, NY.
18. Meritxell Batch Cuadra, Claudio Pollo,Anton Bardera, Olivier Cuisenaire,Jean-Guy Villemure, and Jean-Philippe Thiran, “Atlas-Based Segmentation of Pathological MR Brain Images using a Model of Lesion Growth” , IEEE Transactions on Medical Imaging, vol.23,No.10, October 2004, pp.1301-1314.
19. Raz Koren and Yitzhak Yitzhaky, “Automatic selection of edge detector parameters based on spatial and statistical measures”, Computer Vision and Image Understanding 102 (2006), Elsevier, pp.204-213.
20. Christopher L Gilchrist, Jessie Q Xia, Lori A Stton and Edword W Hsu, “High_Resolution Determination of soft Tissue Deforminations using MRI and First-Order Texture Correlation”, IEE Transactions on Medical Imaging, Vol.23, No.5, May 2004, pp 546-553.
21. Otsu, N. "A Threshold Selection Method from Gray-Level Histograms," IEEE Transactions on. Systems, Man, and Cybernetics, vol. SMC-9, no. 1, pp. 62-66.
22. Francois Chabaf, David M. Hansell, Guang-Zhong Yang “Computerized Decision Support in Medical Imaging” IEEE Engineering In Medicine And Biology , 2000, pp.89-96
23. The Mathworks, Using Matlab Graphics
24. The Mathworks, Creating Graphics User Interfaces
25. 3. Marchand Patrick, Holland Thomas O., Graphics and GUIs with Matlab, 3ed, 2003, CRC Press.
26. Kris Luyten*, Jan Van den Bergh, Chris Vandervelpen, Karin Coninx, “Designing distributed user interfaces for ambient intelligent environments using models and simulations” Computers & Graphics 30 (2006), pp. 702-713.

Graphical User Interface (GUI) Intelligent Diagnostic Imaging system (IDIS) Magnetic Resonant Imaging (MRI) Central Nervous System (CNS) Cerebro-Spinal Fluid (CSF) Fluid Attenuating Inversion Recovery (FLAIR)