Classification of MRI Brain Image using SVM Classifier

Author(s):

Ashish Bhanudas Mane , PDEA’s COEM, Savitribai Phule University of Pune, Pune; Mrs. M. C. Hingane, PDEA’s COEM, Pune; Mr.Satish Matkar, PDEA’s COEM, Pune; Mr. Ambadas Shirsat, PDEA’s COEM, Pune

Keywords:

Feature Extraction, GLCM, Image Retrieval, MRI, SVM Classifier

Abstract:

In this paper we describe the design and development of content-based image retrieval (CBIR) system. This system enables both multi-image query and slide –level image retrieval in order to protect the semantic consistency among the retrieved images. Content of image retrieval is the process of finding relevant image from large collection of image database using visual queries. Medical images have led to growth in large image collection. To enhance the medical image retrieval for diagnostics, research and teaching purposes is done by CBIR. The system performance is improved by the multiple image queries instead of single image query. Pre-processing of the query image is done by median filter to remove the noise. Then the filtered image is given as input to the feature extraction technique which is a transformation of input image into set of features such as texture and shape. Feature extraction is done by the Gray level co-occurrence matrix algorithm that contains information about the position of pixels having similar gray level values. SVM (Support Vector machine) classifier is to group items that have similar feature values into three categories such as normal, benign and malignant. Then SVM classifier is followed by KNN (K-nearest neighbour) which search the corresponding database index will be computed by similarity feature matching. The query image is classified by the classifier to a particular class and the relevant images are retrieved from the database.

Other Details:

Manuscript Id	:	IJSTEV1I9017
Published in	:	Volume : 1, Issue : 9
Publication Date	:	01/04/2015
Page(s)	:	24-28

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