Independent Component Analysis based Denoising of Magnetic Resonance Images

Denoising is an essential step for most of the digital image processing systems. Image denoising involves the manipulation of the image data to produce a visually high quality image. MRI images are always corrupted by random noises. In denoising of magnetic resonance images it is very important to p...

Full description

Saved in:
Bibliographic Details
Published in:International journal of computer applications 2012-01, Vol.54 (2), p.13-18
Main Authors: Sukhatme, Neelabh, Shukla, Shailja
Format: Article
Language:English
Subjects:
Discrete Wavelet Transform
Independent component analysis
Magnetic resonance
Mathematical models
Noise
Preserves
Signal to noise ratio
Citations: Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by cdi_FETCH-LOGICAL-c1708-810621ab0302f19dc7ce283e50133984b3e9ee00a87ade1d77a5b1f29c7b5f6d3
cites
container_end_page 18
container_issue 2
container_start_page 13
container_title International journal of computer applications
container_volume 54
creator Sukhatme, Neelabh
Shukla, Shailja
description Denoising is an essential step for most of the digital image processing systems. Image denoising involves the manipulation of the image data to produce a visually high quality image. MRI images are always corrupted by random noises. In denoising of magnetic resonance images it is very important to preserve the useful details rather than just increasing its peak signal to noise ratio (PSNR) value. Different noise models including additive and multiplicative types are used. They include Gaussian noise, salt and pepper noise, speckle noise. This paper uses Independent component analysis (ICA) for denoising of noisy MRI's. A comparative analysis was also performed, the output obtained by independent component analysis were compared with that obtained from discrete wavelet transform (DWT). The comparative analysis shows that the independent component analysis is better than the discrete wavelet transform in terms of peak signal to noise ratio (PSNR), Mean square error (MSE) and Mean structural similarity index metric (MSSIM).
doi_str_mv 10.5120/8537-2078
format article
fullrecord <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_1283723656</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>1283723656</sourcerecordid><originalsourceid>FETCH-LOGICAL-c1708-810621ab0302f19dc7ce283e50133984b3e9ee00a87ade1d77a5b1f29c7b5f6d3</originalsourceid><addsrcrecordid>eNpdkD1PwzAQhi0EElXpwD-wxAJDwB91bI9VoVBRhIRgthznUqVK7JBrh_57EpUBccPdOzx6pXsIuebsXnHBHoySOhNMmzMyYVarzBijz__kSzJD3LFhpBW5nU_I6zqW0MGw4p4uU9ulOKZF9M0Ra6SFRyjpI8RUYx23NFX0zW8j7OtAPwBT9DEAXbd-C3hFLirfIMx-75R8rZ4-ly_Z5v15vVxsssA1M5nhLBfcF0wyUXFbBh1AGAmKcSmtmRcSLABj3mhfAi-19qrglbBBF6rKSzklt6ferk_fB8C9a2sM0DQ-Qjqg40ObFjJX-YDe_EN36dAPz42U1FzNlbEDdXeiQp8Qe6hc19et74-OMzeadaNZN5qVPwvWaN8</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1237154589</pqid></control><display><type>article</type><title>Independent Component Analysis based Denoising of Magnetic Resonance Images</title><source>Freely Accessible Journals</source><creator>Sukhatme, Neelabh ; Shukla, Shailja</creator><creatorcontrib>Sukhatme, Neelabh ; Shukla, Shailja</creatorcontrib><description>Denoising is an essential step for most of the digital image processing systems. Image denoising involves the manipulation of the image data to produce a visually high quality image. MRI images are always corrupted by random noises. In denoising of magnetic resonance images it is very important to preserve the useful details rather than just increasing its peak signal to noise ratio (PSNR) value. Different noise models including additive and multiplicative types are used. They include Gaussian noise, salt and pepper noise, speckle noise. This paper uses Independent component analysis (ICA) for denoising of noisy MRI's. A comparative analysis was also performed, the output obtained by independent component analysis were compared with that obtained from discrete wavelet transform (DWT). The comparative analysis shows that the independent component analysis is better than the discrete wavelet transform in terms of peak signal to noise ratio (PSNR), Mean square error (MSE) and Mean structural similarity index metric (MSSIM).</description><identifier>ISSN: 0975-8887</identifier><identifier>EISSN: 0975-8887</identifier><identifier>DOI: 10.5120/8537-2078</identifier><language>eng</language><publisher>New York: Foundation of Computer Science</publisher><subject>Discrete Wavelet Transform ; Independent component analysis ; Magnetic resonance ; Mathematical models ; Noise ; Preserves ; Signal to noise ratio</subject><ispartof>International journal of computer applications, 2012-01, Vol.54 (2), p.13-18</ispartof><rights>Copyright Foundation of Computer Science 2012</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c1708-810621ab0302f19dc7ce283e50133984b3e9ee00a87ade1d77a5b1f29c7b5f6d3</citedby></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27922,27923</link.rule.ids></links><search><creatorcontrib>Sukhatme, Neelabh</creatorcontrib><creatorcontrib>Shukla, Shailja</creatorcontrib><title>Independent Component Analysis based Denoising of Magnetic Resonance Images</title><title>International journal of computer applications</title><description>Denoising is an essential step for most of the digital image processing systems. Image denoising involves the manipulation of the image data to produce a visually high quality image. MRI images are always corrupted by random noises. In denoising of magnetic resonance images it is very important to preserve the useful details rather than just increasing its peak signal to noise ratio (PSNR) value. Different noise models including additive and multiplicative types are used. They include Gaussian noise, salt and pepper noise, speckle noise. This paper uses Independent component analysis (ICA) for denoising of noisy MRI's. A comparative analysis was also performed, the output obtained by independent component analysis were compared with that obtained from discrete wavelet transform (DWT). The comparative analysis shows that the independent component analysis is better than the discrete wavelet transform in terms of peak signal to noise ratio (PSNR), Mean square error (MSE) and Mean structural similarity index metric (MSSIM).</description><subject>Discrete Wavelet Transform</subject><subject>Independent component analysis</subject><subject>Magnetic resonance</subject><subject>Mathematical models</subject><subject>Noise</subject><subject>Preserves</subject><subject>Signal to noise ratio</subject><issn>0975-8887</issn><issn>0975-8887</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2012</creationdate><recordtype>article</recordtype><recordid>eNpdkD1PwzAQhi0EElXpwD-wxAJDwB91bI9VoVBRhIRgthznUqVK7JBrh_57EpUBccPdOzx6pXsIuebsXnHBHoySOhNMmzMyYVarzBijz__kSzJD3LFhpBW5nU_I6zqW0MGw4p4uU9ulOKZF9M0Ra6SFRyjpI8RUYx23NFX0zW8j7OtAPwBT9DEAXbd-C3hFLirfIMx-75R8rZ4-ly_Z5v15vVxsssA1M5nhLBfcF0wyUXFbBh1AGAmKcSmtmRcSLABj3mhfAi-19qrglbBBF6rKSzklt6ferk_fB8C9a2sM0DQ-Qjqg40ObFjJX-YDe_EN36dAPz42U1FzNlbEDdXeiQp8Qe6hc19et74-OMzeadaNZN5qVPwvWaN8</recordid><startdate>20120101</startdate><enddate>20120101</enddate><creator>Sukhatme, Neelabh</creator><creator>Shukla, Shailja</creator><general>Foundation of Computer Science</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope></search><sort><creationdate>20120101</creationdate><title>Independent Component Analysis based Denoising of Magnetic Resonance Images</title><author>Sukhatme, Neelabh ; Shukla, Shailja</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c1708-810621ab0302f19dc7ce283e50133984b3e9ee00a87ade1d77a5b1f29c7b5f6d3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2012</creationdate><topic>Discrete Wavelet Transform</topic><topic>Independent component analysis</topic><topic>Magnetic resonance</topic><topic>Mathematical models</topic><topic>Noise</topic><topic>Preserves</topic><topic>Signal to noise ratio</topic><toplevel>online_resources</toplevel><creatorcontrib>Sukhatme, Neelabh</creatorcontrib><creatorcontrib>Shukla, Shailja</creatorcontrib><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts – Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>International journal of computer applications</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Sukhatme, Neelabh</au><au>Shukla, Shailja</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Independent Component Analysis based Denoising of Magnetic Resonance Images</atitle><jtitle>International journal of computer applications</jtitle><date>2012-01-01</date><risdate>2012</risdate><volume>54</volume><issue>2</issue><spage>13</spage><epage>18</epage><pages>13-18</pages><issn>0975-8887</issn><eissn>0975-8887</eissn><abstract>Denoising is an essential step for most of the digital image processing systems. Image denoising involves the manipulation of the image data to produce a visually high quality image. MRI images are always corrupted by random noises. In denoising of magnetic resonance images it is very important to preserve the useful details rather than just increasing its peak signal to noise ratio (PSNR) value. Different noise models including additive and multiplicative types are used. They include Gaussian noise, salt and pepper noise, speckle noise. This paper uses Independent component analysis (ICA) for denoising of noisy MRI's. A comparative analysis was also performed, the output obtained by independent component analysis were compared with that obtained from discrete wavelet transform (DWT). The comparative analysis shows that the independent component analysis is better than the discrete wavelet transform in terms of peak signal to noise ratio (PSNR), Mean square error (MSE) and Mean structural similarity index metric (MSSIM).</abstract><cop>New York</cop><pub>Foundation of Computer Science</pub><doi>10.5120/8537-2078</doi><tpages>6</tpages><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier ISSN: 0975-8887
ispartof International journal of computer applications, 2012-01, Vol.54 (2), p.13-18
issn 0975-8887
0975-8887
language eng
recordid cdi_proquest_miscellaneous_1283723656
source Freely Accessible Journals
subjects Discrete Wavelet Transform
Independent component analysis
Magnetic resonance
Mathematical models
Noise
Preserves
Signal to noise ratio
title Independent Component Analysis based Denoising of Magnetic Resonance Images
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-09T14%3A58%3A07IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Independent%20Component%20Analysis%20based%20Denoising%20of%20Magnetic%20Resonance%20Images&rft.jtitle=International%20journal%20of%20computer%20applications&rft.au=Sukhatme,%20Neelabh&rft.date=2012-01-01&rft.volume=54&rft.issue=2&rft.spage=13&rft.epage=18&rft.pages=13-18&rft.issn=0975-8887&rft.eissn=0975-8887&rft_id=info:doi/10.5120/8537-2078&rft_dat=%3Cproquest_cross%3E1283723656%3C/proquest_cross%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c1708-810621ab0302f19dc7ce283e50133984b3e9ee00a87ade1d77a5b1f29c7b5f6d3%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=1237154589&rft_id=info:pmid/&rfr_iscdi=true