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Multi-Classifier Framework for Medical Image Analysis using Mutual Information Criterion

International Journal of Computer Applications
© 2013 by IJCA Journal
Volume 61 - Number 4
Year of Publication: 2013
Edbert Rajan. R
Prasadh. K

Edbert Rajan. R and Prasadh. K. Article: Multi-Classifier Framework for Medical Image Analysis using Mutual Information Criterion. International Journal of Computer Applications 61(4):15-22, January 2013. Full text available. BibTeX

	author = {Edbert Rajan. R and Prasadh. K},
	title = {Article: Multi-Classifier Framework for Medical Image Analysis using Mutual Information Criterion},
	journal = {International Journal of Computer Applications},
	year = {2013},
	volume = {61},
	number = {4},
	pages = {15-22},
	month = {January},
	note = {Full text available}


The efficacy of the medical image analysis with the level-set shape along with fractal texture and intensity features to discriminate PF (Posteriorfossa) tumor from other tissues in the brain image. Further, extracted features may not be adequate to differentiate amongst the medical images. To enhance the medical image processing, 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, it is necessary to classify the image based on different classes it belongs to. To classify a medical image, a multiple classifier framework and classify the image based on the classes like normal body cells, infected cells, and highly infected cells. The classifier is designed based on the mutual information coefficient of the selected features underwent for image segmentation procedures. The classification is done with set of rotation invariant features being selected on the lines of subjective-optimality and different classifiers are organized using different features sets trained in different data. An experimental performance is evaluated with benchmark data sets extracted from research repositories of both real and synthetic data sets. The performance parameter used for the analysis of the proposed multi-classifier framework using mutual concept criterion [MFMCC] for medical image analysis are Multiple Class intensity, Mutual information coefficient of rich features and efficiency.


  • Dame, A. et. Al. , "Second-Order Optimization of Mutual Information for Real-Time Image Registration", IEEE Transactions on Image Processing, 2012
  • Loeckx, D. et. Al. , "Nonrigid Image Registration Using Conditional Mutual Information", IEEE Transactions on Medical Imaging, 2010
  • Ashraf, A. et. Al. , "A Multichannel Markov Random Field Framework for Tumor Segmentation with an Application to Classification of Gene Expression-based Breast Cancer Recurrence Risk. ", IEEE Transactions on Medical Imaging, 2012
  • Kerroum, M. A. et. Al. , "Textural feature selection by mutual information for multispectral image classification", International Conference on Multimedia Computing and Systems (ICMCS) 2009.
  • Hossain, M. A. et. Al. , "Unsupervised feature extraction based on a mutual information measure for hyperspectral image classification", IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2011
  • Hu, M. et. Al. , "Maximisation of mutual information for gait-based soft biometric classification using gabor features", Biometrics, IET, 2012
  • Carmona, P. L. et. Al. , "Band selection in spectral imaging for classification and regression tasks using information theoretic measures", 10th Euro-American Workshop on Information Optics (WIO), 2011
  • Qiang Wang et. Al. , "Shared chain mutual information measure for image fusion", IEEE International Conference on Automation and Logistics, 2008. ICAL 2008.
  • Michel, V. et. Al. , "Mutual information-based feature selection enhances FMRI brain activity classification", 5th IEEE International Symposium on Biomedical Imaging, 2008.
  • El Maia, H. et. Al. , "Hybridization of color information with texture by using mutual information for the classification of satellite images", International Conference on Multimedia Computing and Systems, 2009. ICMCS '09.
  • H. P. Ng et. Al. , "Medical Image Segmentation using K-means Clustering and Improved Watershed Algorithm", IEEE Southwest Symposium on Image Analysis and Interpretation, 2006
  • Gurbinder Kaur et. Al. , "Intensity based Image Segmentation using Wavelet Analysis and Clustering Techniques", Indian Journal of Computer Science and Engineering (IJCSE) ISSN: 0976-5166 Vol. 2 No. 3 Jun-Jul 2011