April 2013 Issue Vol.3 No.4
1M.E (control and instrumentation) PG Scholar, Department of Electrical and Electronics Engineering,Anna University of technology, Coimbatore.
2B.E (Computer Science Engineering) UG Scholar, Department of Computer Science Engineering,kalaignar karunanidhi institute of technology,Coimbatore.
Abstract: The method of human analysis on medical images is the difficult task. This is mainly due to very minute variations and the co-resemblance between affected & original biological part and it also requires a larger data set for analysis. This makes the biological analysis for prediction a tedious one. This problem grows complicated under the prediction of cancer basically in brain. The challenging task is to develop an automated recognition system which could process on a large information of patient and provide a correct estimation of the results. This project deals with an automated cancer recognition system for MRI images. We implement the neural network algorithm on the given MRI image for the classification and estimation of affected regions.
Keywords: Co-resemblance, MRI images, Neural Network, Biological analysis, Backpropogation.
Research Scholar,Anna University, Chennai, Tamil Nadu, India
Dean,Department of Science and Humanities,Nehru Institute of Technology,Kaliyapalayam, Coimbatore – 105, India
Abstract: In this paper, we have proposed a model of teeth recognition to identify a person. The teeth image of a person is matched against the teeth image database. We have developed an algorithm to recognize the teeth using image processing techniques. The proposed work is an application of pattern recognition, which analyses the pattern of teeth images. A similarity criterion has been derived to match against the specified threshold value. This similarity measure has been used for person identification. The experiment results has been carried on 20 teeth images of the same person and 100 teeth images of different persons from our database and Labial Teeth Database of Color Imaging Lab - University of Granada – Spain. MATLAB 7.4 has been used for this purpose. The paper is described in different sections: section I introduces the proposed system of teeth recognition. In section II, we have proposed the model for teeth recognition, methodology and working of the proposed algorithm. We have analyzed the results in section III. Finally, section IV provides a conclusion.
Keywords: Dental identity, Forensic Dental Biometric, Dental Biometric, Segmentation, Matching, image processing.