Investigation on Feature Extraction and Classification of Medical Images

In this paper we present the deep study about the Bio- Medical Images and tag it with some basic extracting features (e.g. color, pixel value etc). The classification is done by using a nearest neighbor classifier with various distance measures as well as the automatic combination of classifier results. This process selects a subset of relevant features from a group of features of the image. It also helps to acquire better understanding about the image by describing which the important features are. The accuracy can be improved by increasing the number of features selected. Various types of classifications were evolved for the medical images like Support Vector Machine (SVM) which is used for classifying the Bacterial types. Ant Colony Optimization method is used for optimal results. It has high approximation capability and much faster convergence, Texture feature extraction method based on Gabor wavelets etc..

Authors:

• P. Gnanasekar
• A. Nagappan
• S. Sharavanan
• O. Saravanan
• D. Vinodkumar
• T. Elayabharathi
• G. Karthik

Keywords:

• ACO Ant Colony Optimization
• Correlogram
• CCM Co-Occurrence Matrix
• RTS Rough-Set theory

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