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Abstract
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Article Information:
Automative Multi Classifier Framework for Medical Image Analysis
R. Edbert Rajan and K. Prasadh
Corresponding Author: R. Edbert Rajan
Submitted: June 14, 2014
Accepted: July 19, 2014
Published: April 25, 2015 |
Abstract:
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Medical image processing is the technique used to create images of the human body for medical purposes. Nowadays, medical image processing plays a major role and a challenging solution for the critical stage in the medical line. Several researches have done in this area to enhance the techniques for medical image processing. However, due to some demerits met by some advanced technologies, there are still many aspects that need further development. Existing study evaluate the efficacy of the medical image analysis with the level-set shape along with fractal texture and intensity features to discriminate PF (Posterior Fossa) tumor from other tissues in the brain image. To develop the medical image analysis and disease diagnosis, to devise an automotive subjective optimality model for segmentation of images based on different sets of selected features from the unsupervised learning model of extracted features. After segmentation, classification of images is done. The classification is processed by adapting the multiple classifier frameworks in the previous work based on the mutual information coefficient of the selected features underwent for image segmentation procedures. In this study, to enhance the classification strategy, we plan to implement enhanced multi classifier framework for the analysis of medical images and disease diagnosis. The performance parameter used for the analysis of the proposed enhanced multi classifier framework for medical image analysis is Multiple Class intensity, image quality, time consumption.
Key words: Feature extraction, feature selection, medical image analysis, mutual information, scale invariant feature extraction, ,
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Cite this Reference:
R. Edbert Rajan and K. Prasadh, . Automative Multi Classifier Framework for Medical Image Analysis. Research Journal of Applied Sciences, Engineering and Technology, (12): 1051-1057.
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ISSN (Online): 2040-7467
ISSN (Print): 2040-7459 |
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