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Retrieval of MRI Images Using CBIR Techniques and Improved KNN Algorithm


Affiliations
1 Manoharbhai Patel Institute of Engineering and Technology(MIET), Dept. of Information Technology, Gondia, Maharastra, India
2 School of Engineering & IT, Dept. of Computer Technology, MATS University, Raipur, Chhattisgarh, India
     

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CBIR (Content Based Image Retrieval) is an technique used for the retrieval of desired images from a large collection of images in a database on the basis of  image features such as coloration, surface as well as form that can be easily and efficiently extracted from the images themselves. In many current applications with large image databases, traditional methods of image retrieval and processing have proven to be insufficient. K-Nearest Neighbour (KNN) is a very powerful and popular classification algorithm to classify image data. However, this algorithm has its own limitations in certain situations. In this paper, an improved algorithm has been proposed that focuses on providing improved training set to KNN which is less in size  and more accurate combined with a improved SFTA algorithm to extract features that helps to extract training set which is better and reduces the execution time of KNN algorithm..


Keywords

MRI Processing, Improved KNN, SFTA.
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  • Retrieval of MRI Images Using CBIR Techniques and Improved KNN Algorithm

Abstract Views: 147  |  PDF Views: 3

Authors

Rashmi Priyanka
Manoharbhai Patel Institute of Engineering and Technology(MIET), Dept. of Information Technology, Gondia, Maharastra, India
Sandeep Gonnade
School of Engineering & IT, Dept. of Computer Technology, MATS University, Raipur, Chhattisgarh, India

Abstract


CBIR (Content Based Image Retrieval) is an technique used for the retrieval of desired images from a large collection of images in a database on the basis of  image features such as coloration, surface as well as form that can be easily and efficiently extracted from the images themselves. In many current applications with large image databases, traditional methods of image retrieval and processing have proven to be insufficient. K-Nearest Neighbour (KNN) is a very powerful and popular classification algorithm to classify image data. However, this algorithm has its own limitations in certain situations. In this paper, an improved algorithm has been proposed that focuses on providing improved training set to KNN which is less in size  and more accurate combined with a improved SFTA algorithm to extract features that helps to extract training set which is better and reduces the execution time of KNN algorithm..


Keywords


MRI Processing, Improved KNN, SFTA.