Research Article
Assessment of Diverse Quality Metrics for Medical Images Including Mammography
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
|
| Volume 83 - Issue 4 |
| Published: December 2013 |
| Authors: T. Venkat Narayana Rao, A. Govardhan |
10.5120/14440-2593
|
T. Venkat Narayana Rao, A. Govardhan . Assessment of Diverse Quality Metrics for Medical Images Including Mammography. International Journal of Computer Applications. 83, 4 (December 2013), 42-47. DOI=10.5120/14440-2593
@article{ 10.5120/14440-2593,
author = { T. Venkat Narayana Rao,A. Govardhan },
title = { Assessment of Diverse Quality Metrics for Medical Images Including Mammography },
journal = { International Journal of Computer Applications },
year = { 2013 },
volume = { 83 },
number = { 4 },
pages = { 42-47 },
doi = { 10.5120/14440-2593 },
publisher = { Foundation of Computer Science (FCS), NY, USA }
}
%0 Journal Article
%D 2013
%A T. Venkat Narayana Rao
%A A. Govardhan
%T Assessment of Diverse Quality Metrics for Medical Images Including Mammography%T
%J International Journal of Computer Applications
%V 83
%N 4
%P 42-47
%R 10.5120/14440-2593
%I Foundation of Computer Science (FCS), NY, USA
Abstract
This paper presents the comparative analysis of various quality metrics for medical image processing. Measurement of image quality is vital for numerous image-processing applications. Image quality measurement is closely related to image resemblance and assessment in which quality is based on the differences (or similarity) between a degraded image and that of the original image i. e. unmodified image chiefly in mammographic images. We have employed simple verifiable techniques for representing the image quality rather than difficult mathematical procedures, which are costly, time consuming and observer dependent. In this paper, the images have been subjected to various degrees of blur, noise, compression , contrast levels . Based on these factors the quality has been measured in terms of metrics like Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR), Maximum difference (MD) including new metric of image qualities such as Structural Similarity Index Metrics (SSIM)for low cost medical image analysis.
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