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Review of improved A.I. based Image Segmentation for medical diagnosis applications

P. Ranjan1 , P.R. Khan2

Section:Review Paper, Product Type: Journal Paper
Volume-4 , Issue-11 , Page no. 75-81, Nov-2016

Online published on Nov 29, 2016

Copyright © P. Ranjan, P.R. Khan . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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IEEE Style Citation: P. Ranjan, P.R. Khan, “Review of improved A.I. based Image Segmentation for medical diagnosis applications,” International Journal of Computer Sciences and Engineering, Vol.4, Issue.11, pp.75-81, 2016.

MLA Style Citation: P. Ranjan, P.R. Khan "Review of improved A.I. based Image Segmentation for medical diagnosis applications." International Journal of Computer Sciences and Engineering 4.11 (2016): 75-81.

APA Style Citation: P. Ranjan, P.R. Khan, (2016). Review of improved A.I. based Image Segmentation for medical diagnosis applications. International Journal of Computer Sciences and Engineering, 4(11), 75-81.

BibTex Style Citation:
@article{Ranjan_2016,
author = {P. Ranjan, P.R. Khan},
title = {Review of improved A.I. based Image Segmentation for medical diagnosis applications},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {11 2016},
volume = {4},
Issue = {11},
month = {11},
year = {2016},
issn = {2347-2693},
pages = {75-81},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=1111},
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=1111
TI - Review of improved A.I. based Image Segmentation for medical diagnosis applications
T2 - International Journal of Computer Sciences and Engineering
AU - P. Ranjan, P.R. Khan
PY - 2016
DA - 2016/11/29
PB - IJCSE, Indore, INDIA
SP - 75-81
IS - 11
VL - 4
SN - 2347-2693
ER -

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Abstract

Image segmentation is very important application in a biomedical diagnosis use image data analysis. In medical analysis the accuracy of image segmentation has a critical clinical requirement for the localization of body organs or pathologies in order to raise the quality of prediction of disease or infections. This paper covers review that includes several articles in which latest A.I biomedical image segmentation techniques are applied to different imaging color space models. This review article describes how various computer assisted diagnosis system works for achieving the goal of finding abnormal segments of body organs in biomedical images of the MRI, ultrasound etc. It has been observed that those segmentation approach are broadly giving accurate results in which the segmentation of the images is performed by defining an active shape model and then localization of potential area of interest using thresholding.

Key-Words / Index Term

Image processing, biomedical analysis, detection, pattern recognition

References

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