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Tumor Identification in CT Medical Images using Semi Automatic Active Contour Models
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 96 - Number 17 |
| Year of Publication: 2014 |
| Authors: P. V. V. Kishore, Sampath Kumar, Y. Sahithi Reddy, J. K. Chaithanya, M. V. D. Prasad |
10.5120/16884-6892
|
P. V. V. Kishore, Sampath Kumar, Y. Sahithi Reddy, J. K. Chaithanya, M. V. D. Prasad . Tumor Identification in CT Medical Images using Semi Automatic Active Contour Models. International Journal of Computer Applications. 96, 17 ( June 2014), 8-14. DOI=10.5120/16884-6892
Abstract
Damage of nerves occurs when an external force traumatically injures the nerves which cause a host of physical, cognitive and behavioral effects can range from complete recovery or permanent disability or death. The delineation of the trauma in brain, abdomen and lungs is a challenging task due to variations in shapes and image quality. In this method we present a semi automatic method for extracting the damage of nerves in brain, abdomen and lung from cardiac CT images, in which all the nerves are located sequentially. Each nerve is detected by first identifying the injuries and then segmenting the damaged part. To this end, the damaged nerve is localized by utilizing its geometric features obtained on-line from a CT image. After that, a variational region-growing model is employed to extract the damages. In particular, the location of the nerves damage is determined using via active contour model on the surface of the nerves. This extracts the damage of the nervous system and determines the exact accuracy of the damage is demonstrated by experimental results.
References
- Maintz, J. B. , and Max A. Viergever. "A survey of medical image registration. "Medical image analysis 2. 1 (1998): 1-36.
- McAuliffe, Matthew J. , et al. "Medical image processing, analysis and visualization in clinical research. " Computer-Based Medical Systems, 2001. CBMS 2001. Proceedings. 14th IEEE Symposium on. IEEE, 2001.
- Lehmann, Thomas Martin, Claudia Gonner, and Klaus Spitzer. "Survey: Interpolation methods in medical image processing. " Medical Imaging, IEEE Transactions on 18. 11 (1999): 1049-1075.
- Studholme, Colin, Derek LG Hill, and David J. Hawkes. "Automated 3-D registration of MR and CT images of the head. " Medical image analysis 1. 2 (1996): 163-175.
- D. Terzopoulos and K. Fleischer (1988) "Deformable models". The Visual Computer, Vol. 4 No. 6, pp306-331.
- P. V. V. Kishore, P. Rajesh Kumar,"Sign Language Video Segmentation with Level Sets Fusing Color, Texture, Boundary and Shape Features" Signal & Image Processing an International Journal, AIRCCSE Publishers, SIPIJ, June 2012, Vol(3), No. (3), ISSN: 2229 – 3922, pp73-92.
- P. V. V. Kishore, P. Rajesh Kumar, "Tracking Complex Movements Under Assorted Backgrounds in Sign Language Using Active Contour Models" CiiT International Journal of Digital Image Processing, ISSN 0974 – 9586, Vol(4),Issue(6), DOI: DIP062012009.
- P. V. V. Kishore, P. Rajesh Kumar, " Segment, Track, Extract, Recognize and Convert Sign Language Videos to Voice/Text" International Journal of Advanced Computer Science and Applications (IJACSA) ISSN(Print)-2156 5570, ISSN(Online)- 2158-107x, June 2012 Edition Vol(3), No 6, pp35-47.
- Luminita Vese and Tony Chan. (2002) "A multiphase level set framework for image segmentation using the mumford and shah model". International Journal of Computer Vision, Vol. 50,No. 3 pp271-293.
- Pelizzari, Charles A. , George TY Chen, Danny R. Spelbring, Ralph R. Weichselbaum, and Chin-Tu Chen. "Accurate three-dimensional registration of CT, PET, and/or MR images of the brain. " Journal of computer assisted tomography 13, no. 1 (1989): 20-26.
- Qing-song Zhu, Yao-qin Xie, Lei Wang (2010) Video Object Segmentation by Fusion of Spatio-Temporal Information Based on Gaussian Mixture Model, Bulletin of advanced technology research, vol. 5, No. 10, pp38-43.
- Wen-kai Tsai, Chung-chi Lin, Shyue-wen Yang, Ming-hwa Sheu, Ching-lung Su (2008), "Adaptive Motion Gesture Segmentation". The 2008 International Conference on Embedded Software and Systems Symposia (ICESS2008), pp386-391.
- N. Peyrard and P. Bouthemy (2002) "Content-based video segmentation using statistical motion models". In Proc. BMVC, Cardiff, pages 527–536.
- Ginmo Chung and Luminita Vese. (2003) "Image segmentation using a multilayer level set approach. Technical Report" 03-53, UCLA.
- Armato III, Samuel G. , and William F. Sensakovic. "Automated lung segmentation for thoracic CT: Impact on computer-aided diagnosis1. " Academic Radiology 11, no. 9 (2004): 1011-1021.
- Prastawa, Marcel, Elizabeth Bullitt, Sean Ho, and Guido Gerig. "A brain tumor segmentation framework based on outlier detection. " Medical Image Analysis 8, no. 3 (2004): 275-283.
- Sluimer, Ingrid, Mathias Prokop, and Bram van Ginneken. "Toward automated segmentation of the pathological lung in CT. " Medical Imaging, IEEE Transactions on 24, no. 8 (2005): 1025-1038.
- P. V. V. Kishore, P. Rajesh Kumar, "Segment, Track, Extract, Recognize and Convert Sign Language Videos to Voice/Text" International Journal of Advanced Computer Science and Applications (IJACSA) ISSN(Print)-2156 5570, ISSN(Online)- 2158-107x, June 2012 Edition Vol(3), No 6, pp35-47.
- P. V. V. Kishore, S. R. C. Kishore, M. V. D. Prasad, "Conglomeration of Hand Shapes and Texture Information for Recognizing Gestures of Indian Sign Language Using Feed forward Neural Networks", International Journal of engineering and Technology (IJET), ISSN: 0975-4024, Vol 5 No 5 Oct-Nov 2013, pp3742-3756.
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