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Content Based Medical Image Retrieval using Artificial Neural Network


Author(s):

Preetika D'Silva , Rajarajeswari College of Engineering; P.Bhuvaneswari, Rajarajeswari College of Engineering

Keywords:

Content based Medical Image Retrieval, Euclidean Distance GLCM, Neural network, Zernike

Abstract:

Large database of images in the field of medicine requires proper systems that will help in accurate diagnostics and their efficient management. Content based medical image retrieval is a system that helps to browse, explore, find, and retrieve images similar to the query image with minimal user input. In this paper we propose a system that will retrieve all medical images that matches the query image. Shape and texture features are extracted from the pre-processed medical images for creating the medical database. Once the medical database is created, the features of the query image are extracted and are used by the neural network to train it. Euclidean distance between the database features and the query features are computed, ranked and we label the relevant images from the initial retrieved images. Then the feed forward back propagation neural network is used finally to retrieve the similar medical images. We have taken X-ray images of hand, foot, chest, head and ankle. The precision and recall values for the retrieval system using only texture features, using only shape features and using combined texture and shape features are calculated and compared.


Other Details:

Manuscript Id :IJSTEV1I11115
Published in :Volume : 1, Issue : 11
Publication Date: 01/06/2015
Page(s): 338-343
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