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Home / programming language / Online Matlab Projects / MRI Brain Image Retrieval Using Multi Support Vector Machine Classifier
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MRI Brain Image Retrieval Using Multi Support Vector Machine Classifier

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SKU: PROJ1215 Categories: 2013 Projects, Digital Image Processing Projects, Final Year Projects, Online Matlab Projects Tags: academic projects, Android project 2013-2014, Android project Abstract, Android project list, btech projects, dotnet project 2013-2014, dotnet project Abstract, dotnet project list, Final Year Projects, ieee abstracts, ieee paper download, ieee projects 2013, ieee projects 2013 paper list, ieee projects 2013-2014, ieee projects 2014, ieee projects 2014 paper list, ieee projects 2015, ieee projects 2015 paper list., ieee titles, java project list, java projects 2013-2014, java projects Abstract, madurai software company, matlab project 2013-2014, matlab project Abstract, matlab project list, mtech projects, phd research work, Php project 2013-2014, Php project Abstract, Php project list, Power Electronic project 2013-2014, Power Electronic project Abstract, Power Electronic project list, project center Bangalore, project center chennai, project center coimbatore, project center Erode, project center Hyderabad, project center Kollam, project center madurai, project center Pandicherry, project center ramnad, project center Salem, project center Tiruneveli, project center trichy, research center, Students Projects, Vlsi project 2013-2014, Vlsi project Abstract, Vlsi project list
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MRI Brain Image Retrieval Using Multi Support Vector Machine Classifier

Abstract— 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 content of medical images is difficult to describe in words or textual form. The proposed system uses multiple image queries for finding desired images from the database.< Final Year Projects > 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.The feature optimization is done on the extracted features to select best features out of it to train the classifier. SVM (Support Vector machine) classifier is to group items that have similar feature values into three categories such as normal, benign and malignant.

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