PUMA publications for /user/thomasrichter/tomography;jpeg2000;imagehttps://puma.ub.uni-stuttgart.de/user/thomasrichter/tomography;jpeg2000;imagePUMA RSS feed for /user/thomasrichter/tomography;jpeg2000;image2024-03-28T12:27:22+01:00A Comparison of Three Image Fidelity Metrics of Different Computational Principles for JPEG2000 Compressed Abdomen CT Imageshttps://puma.ub.uni-stuttgart.de/bibtex/2b3bb778cf3609f98508db5321c4a6f3c/thomasrichterthomasrichter2016-03-10T09:18:49+01:00image science;Hospitals;Image signal-to-noise visual compression;diagnostic metric;Adult;Area metrics;multiscale organs;computerised tomography;high-dynamic coding;Medical compression;image Curve;Radiography, coding;Computed correlation biological difference X-Ray Compression;Humans;Image imaging;PSNR;Radiology;Transform ratio;Abdomen;Biomedical processing;sensitivity similarity;peak Abdominal;Reproducibility coefficients;abdomen;computed range Curve;Data Nonparametric;Tomography, fidelity coding;medical analysis;CT;HDR-VDP;JPEG2000 Computed compression;MS-SSIM;PSNR;Spearman structural radiography;image tomography;Computer of Under imaging;Computed Results;Statistics, diagnostic predictor;image tomography;JPEG2000;image Processing, tomography;data rank Computer-Assisted;Observation;ROC <span data-person-type="author" class="authorEditorList "><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Kil Joong Kim" itemprop="url" href="/person/139d4c0d08b7efb8cbf0f7a1c55cf1acd/author/0"><span itemprop="name">K. Kim</span></a></span>, </span><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Bohyoung Kim" itemprop="url" href="/person/139d4c0d08b7efb8cbf0f7a1c55cf1acd/author/1"><span itemprop="name">B. Kim</span></a></span>, </span><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="R. Mantiuk" itemprop="url" href="/person/139d4c0d08b7efb8cbf0f7a1c55cf1acd/author/2"><span itemprop="name">R. Mantiuk</span></a></span>, </span><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="T. Richter" itemprop="url" href="/person/139d4c0d08b7efb8cbf0f7a1c55cf1acd/author/3"><span itemprop="name">T. Richter</span></a></span>, </span><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Hyunna Lee" itemprop="url" href="/person/139d4c0d08b7efb8cbf0f7a1c55cf1acd/author/4"><span itemprop="name">H. Lee</span></a></span>, </span><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Heung-Sik Kang" itemprop="url" href="/person/139d4c0d08b7efb8cbf0f7a1c55cf1acd/author/5"><span itemprop="name">H. Kang</span></a></span>, </span><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Jinwook Seo" itemprop="url" href="/person/139d4c0d08b7efb8cbf0f7a1c55cf1acd/author/6"><span itemprop="name">J. Seo</span></a></span>, </span> und <span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Kyoung Ho Lee" itemprop="url" href="/person/139d4c0d08b7efb8cbf0f7a1c55cf1acd/author/7"><span itemprop="name">K. Lee</span></a></span></span>. </span><span class="additional-entrytype-information"><span itemtype="http://schema.org/PublicationIssue" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="journal">Medical Imaging, IEEE Transactions on</span>, </em> <em><span itemtype="http://schema.org/PublicationVolume" itemscope="itemscope" itemprop="isPartOf"><span itemprop="volumeNumber">29 </span></span>(<span itemprop="issueNumber">8</span>):
<span itemprop="pagination">1496-1503</span></em> </span>(<em><span>August 2010<meta content="August 2010" itemprop="datePublished"/></span></em>)</span>Thu Mar 10 09:18:49 CET 2016Medical Imaging, IEEE Transactions on0881496-1503{A} {C}omparison of {T}hree {I}mage {F}idelity {M}etrics of {D}ifferent {C}omputational {P}rinciples for {JPEG}2000 {C}ompressed {A}bdomen {CT} {I}mages292010image science;Hospitals;Image signal-to-noise visual compression;diagnostic metric;Adult;Area metrics;multiscale organs;computerised tomography;high-dynamic coding;Medical compression;image Curve;Radiography, coding;Computed correlation biological difference X-Ray Compression;Humans;Image imaging;PSNR;Radiology;Transform ratio;Abdomen;Biomedical processing;sensitivity similarity;peak Abdominal;Reproducibility coefficients;abdomen;computed range Curve;Data Nonparametric;Tomography, fidelity coding;medical analysis;CT;HDR-VDP;JPEG2000 Computed compression;MS-SSIM;PSNR;Spearman structural radiography;image tomography;Computer of Under imaging;Computed Results;Statistics, diagnostic predictor;image tomography;JPEG2000;image Processing, tomography;data rank Computer-Assisted;Observation;ROC This study aimed to evaluate three image fidelity metrics of different computational principles-peak signal-to-noise ratio (PSNR), high-dynamic range visual difference predictor (HDR-VDP), and multiscale structural similarity (MS-SSIM)-in measuring the fidelity of JPEG2000 compressed abdomen computed tomography images from a viewpoint of visually lossless compression. Three hundred images with 0.67- or 5-mm section thickness were compressed to one of five compression ratios ranging from reversible compression to 15:1. The fidelity of each compressed image was measured by five radiologists' visual analyses (distinguishable or indistinguishable from the original) and the three metrics. The Spearman rank correlation coefficients of the PSNR, HDR-VDP, and MS-SSIM values with the number of readers responding as indistinguishable were 0.86, 0.94, and 0.86, respectively. Using the pooled readers' responses as the reference standard, the area under the receiver-operating-characteristic curve for the HDR-VDP (0.99) was significantly greater than that for the PSNR (0.95) (p <; 0.001) and for the MS-SSIM (0.96) (p = 0.003), and there was no significant difference between the PSNR and MS-SSIM (p = 0.70). In measuring the image fidelity, the HDR-VDP outperforms the PSNR and MS-SSIM, and the MS-SSIM and PSNR are comparable.