International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Monthly Journal for Science , Computer Science and Engineering

Volume5,March 2018,

Topic : Abnormality Feature Extraction in the Spinal Cord MRI Using K-Means Clustering

Authors:S.Shyni Carmel Mary || S.Sasikala

Abstract:Research on Medical Image proposes an efficient platform for automatic analysis and detection of any Deformations in a given medical image data set especially in Spinal Cord for an effective and better understanding of diagnosis. The abnormality of the spinal cord may include Tumor, Disc a hernia, Fracture, swelling etc., which has been detected from any given modality of Medical images such as MRI, CT, and fMRI etc. In this work, Automated Decision support system is introduced for fast and accurate analysis which will help to confirm the existence of affected part of the Spinal Cord MR image. It has two phases. Phase I: Identifying any anomaly features or distortion is found to have existed in the given image or not by using histograms. Phase II: Involves in Clustering of the image which is used to find the depth of the existence of the calcification in the MRI Spinal Image. The performance of the algorithm and the time taken to complete every cluster phase is analyzed. Further, the algorithm’s efficiency is being observed to prove that it gives a perfect accuracy.

Keywords: Medical Images, Image Pre- processing techniques, Histogram, Feature Extraction, K-Means Clustering

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DOI: 01.1617/vol5/iss3/pid37109


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Editor-in-Chief

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Dr. Allon Guez
Professor, Drexel University,
USA


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IMPACT FACTOR: 4.890

ISSN(Online):2394-2320

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Thomson Reuters ID : q-6288-2016.
ORCiD Research ID : 0000-0001-9540-6799

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ACCEPTANCE RATIO: 28.69%
ARTICLES PUBLISHED:0521
PAPER RECEIVED:01730
Journal Code : IJERCSE
Electronic ISSN : 2394-2320
Impact Factor : 4.890
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