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Enhancement Of Edge Detection And Correction Algorithm For Medical Images In Distributed Environment

Abstract:

Image processing plays important role in clinical diagnosis. The MIPAV (Medical Image Processing, Analysis, and Visualization) application enables quantitative analysis and visualization of medical images of numerous modalities such as PET, MRI, CT, or microscopy. It is very difficult to find the problems in Medical images due to noise and irregular structure of human body. The operation Edge detection, Noise reduction,3D rendering and video conversion are performed in medical images. These operations are going to perform in a distributed environment. The distributed environment may be an Grid or Cloud or Cluster. Various tools Globus, Legion, Nimrod and Condor are used as middleware soultion to construct a Medical distributed environment. We are going to perform the operations in these tools and to take analysis report.

Existing System:

Edge detection delivers information about fracture in bone and plays an important role in patient diagnosis for doctors. Bone edge detection in medical images is an important step in image-guided surgery. Human body is constructed by number of bones, veins and muscles. Some of the bones are merged with muscles. Normal eye vision cannot detect the fracture and diagnosis the problem. Different sizes of bones for different individuals in different age make edge detection a challenging area. For example, a child has soft bone and aged people may have hard bone. So, bones in medical images have different sizes and the intensity values of bone pixels are generally non uniform and noisy. It is very difficult to segment the bone by using intensity thresholding. Initial seed search is done by manually. The seed points are found by hounsfield values of pixels. Locating the objects in a noisy or damaged image is very difficult task. Snakes or Active contour model helps to get solution for the above problem. Snakes method is used to locate the objects and to find the boundaries of the object in an image. There are many types of snake are available. GVF (Gradient Vector Flow) forces are used to derive the snake. The GVF object has the ability to both stretch and bend towards the boundaries of the object.

Region growing method finds the exact edge points in an image. The initial set of small areas having similar constraints is merged. Searching the neighboring pixel and comparing it with the current value accomplish the merging. If the values are similar the region will grown. If the value is not equal the region growing process will stop. The process is iterated continuously. In image processing, users normally face "Staircase" or "Jaggies". In Medical imaging these "Staircase" or "Jaggies" are defined as "Interosceal edges". When two bones are either nearby or have an indistinct region separating them, these edges are called as interosceal edges. Apart from detecting interosceal edges, our algorithm finds the knot (Two or more edges met in a point called Knot.) and moves to the neighboring pixel. If third edge is found that edge will be deleted. If indistinct region is found while searching it connects the edge point. This algorithm is implemented in CT and ultrasonic images. The size of the image taken is 167X181 pixel. The time delay taken for segmenting is too long. For 25000 iterations it takes approximately 16 minutes and 28 seconds. When iterations are increased the performance measure is also increased. The major drawback of this algorithm is larger time interval for computing the result. To make segmentation process fast the iterations should be done in high-end computational resource. This advanced algorithm demands for high computing power and storage capacity. The segmentation process can be implemented in distributed computing environment. Proposed System:

Grid computing is an emerging technology to meet the demand on computing power, storage in life science, online games, client modeling and etc. Main focus of grid computing is on demand based resource sharing. Globus, Legion, Nimrod and Condor are examples for middleware solutions used in Grid. Grid Technology can be used to promote biomedical research and clinical works. Medical grids have major impact on the health care business. To decrease the time interval, the segmentation should be carried in remote computational resource in Medical Grid. DICOM (Digital imaging and Communication in medicine) is a protocol used to achieve the reliable transmission in Globus.In the earlier discussion, iterative steps do edge detection. When this work is implemented in Grid environment number of CPU cycles can be made available to achieve to get results fastly. The following method Differential, Log, Canny and Binary morphology plays an important role in edge detection. These methods will be involved in different grid middlewares. Final outputs will be compared.

The GridSphere portal framework provides an open-source portlet based Web portal. GridSphere enables developers to quickly develop and package third-party portlet web applications that can be run and administered within the GridSphere portlet container. New tool GridSphere 3.1 is available

References:

[1] Michal Vossberg, Thomas Tolxdorff, Associate Member, IEEE, and Dagmar Krefting "DICOM Image Communication in Globus Based Medical Grids" IEEE Transactions on Information Technology in Biomedicine Vol 12, No.2, March 2008

[2] Epifanio Bagarinao, Toshiharu Nakai, Yoshio Tanaka "Medical Grid: Using Grid Technology for Brain Studies" Philippine Information Technology Journal. Volume 1, Number 1, February 2008

[3] Tepei Tang Jeng Ku Mobile Digital Hosp. Res. Lab., e-King group, Inc., Taipei; "Mobile Care: Telemedicine Based On Medical Grid and Mobile Network" Wireless and Mobile Computing Conference 2008

[4] M. L. Comer and E. J. Delp, "Segmentation of textured images using a multiresolution gaussian autoregressive model," IEEE Trans. ImageProcess., vol. 8, no. 3, pp. 408-420, Mar. 1999.

[5] M. Kass, A. Witkin, and D. Terzopoulus, "Snake: active contour models," Int. J. Comput. Vis., vol. 1, no. 4, pp. 321-331, 1988.

[6] D. Wang, "A multiscale gradient algorithm for image segmentation using watersheds," Pattern Recognit., vol. 30, no. 12, pp. 2043-2052, 1997.

[7] M. Garza-Jinich, P. Meer, and V. Medina, "Robust retrieval of threedimensional structures from image stacks," Med. Image Anal., vol. 3, no. 1, pp. 21-35, 1999.

[8] R. Adams and L. Bischof, "Seeded region growing," IEEE Trans. Pattern Anal. Mach. Intell., vol. 16, no. 6, pp. 641-647, Jun. 1994.

[9] I. Frosio, Member, IEEE, and N. A. Borghese*, Member, IEEE "Statistical Based Impulsive Noise Removal in Digital Radiography" IEEE Transactions On Medical Imaging, Vol. 28, No. 1, January 2009

[10]. W. Yao, P. Abolmaesumi, Member, IEEE, M. Greenspan, Member, IEEE, and R. E. Ellis*, Member, IEEE "An Estimation/Correction Algorithm for Detecting Bone Edges in CT Images" IEEE Transactions On Medical Imaging, Vol. 24, No. 8, August 2005

[11] Johan Montagnat GARR conference "Health Science Community" Roma, December 12, 2007

[12] J. Montagnat1, V. Breton2, and I.E. Magnin1 "Using grid technologies to face medical image analysis challenges"

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