A Block Adaptive Near-Lossless Compression Algorithm for Medical Image Sequences and Diagnostic Quality Assessment
The near-lossless compression technique has better compression ratio than lossless compression technique while maintaining a maximum error limit for each pixel. It takes the advantage of both the lossy and lossless compression methods providing high compression ratio, which can be used for medical i...
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| Veröffentlicht in: | Journal of digital imaging 2020-04, Vol.33 (2), p.516-530 |
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| creator | Sharma, Urvashi Sood, Meenakshi Puthooran, Emjee |
| description | The near-lossless compression technique has better compression ratio than lossless compression technique while maintaining a maximum error limit for each pixel. It takes the advantage of both the lossy and lossless compression methods providing high compression ratio, which can be used for medical images while preserving diagnostic information. The proposed algorithm uses a resolution and modality independent threshold-based predictor, optimal quantization (
q
) level, and adaptive block size encoding. The proposed method employs resolution independent gradient edge detector (RIGED) for removing inter-pixel redundancy and block adaptive arithmetic encoding (BAAE) is used after quantization to remove coding redundancy. Quantizer with an optimum
q
level is used to implement the proposed method for high compression efficiency and for the better quality of the recovered images. The proposed method is implemented on volumetric 8-bit and 16-bit standard medical images and also validated on real time 16-bit-depth images collected from government hospitals. The results show the proposed algorithm yields a high coding performance with BPP of 1.37 and produces high peak signal-to-noise ratio (PSNR) of 51.35 dB for 8-bit-depth image dataset as compared with other near-lossless compression. The average BPP values of 3.411 and 2.609 are obtained by the proposed technique for 16-bit standard medical image dataset and real-time medical dataset respectively with maintained image quality. The improved near-lossless predictive coding technique achieves high compression ratio without losing diagnostic information from the image. |
| doi_str_mv | 10.1007/s10278-019-00283-3 |
| format | Article |
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q
) level, and adaptive block size encoding. The proposed method employs resolution independent gradient edge detector (RIGED) for removing inter-pixel redundancy and block adaptive arithmetic encoding (BAAE) is used after quantization to remove coding redundancy. Quantizer with an optimum
q
level is used to implement the proposed method for high compression efficiency and for the better quality of the recovered images. The proposed method is implemented on volumetric 8-bit and 16-bit standard medical images and also validated on real time 16-bit-depth images collected from government hospitals. The results show the proposed algorithm yields a high coding performance with BPP of 1.37 and produces high peak signal-to-noise ratio (PSNR) of 51.35 dB for 8-bit-depth image dataset as compared with other near-lossless compression. The average BPP values of 3.411 and 2.609 are obtained by the proposed technique for 16-bit standard medical image dataset and real-time medical dataset respectively with maintained image quality. The improved near-lossless predictive coding technique achieves high compression ratio without losing diagnostic information from the image.</description><identifier>ISSN: 0897-1889</identifier><identifier>EISSN: 1618-727X</identifier><identifier>DOI: 10.1007/s10278-019-00283-3</identifier><identifier>PMID: 31659588</identifier><language>eng</language><publisher>Cham: Springer International Publishing</publisher><subject>Adaptive algorithms ; Algorithms ; Coding ; Compression ; Compression ratio ; Compression tests ; Datasets ; Diagnostic systems ; Image compression ; Image quality ; Imaging ; Measurement ; Medical diagnosis ; Medical imaging ; Medicine ; Medicine & Public Health ; Noise levels ; Optimization ; Pixels ; Quality assessment ; Quality control ; Radiology ; Real time ; Redundancy ; Signal to noise ratio</subject><ispartof>Journal of digital imaging, 2020-04, Vol.33 (2), p.516-530</ispartof><rights>Society for Imaging Informatics in Medicine 2019</rights><rights>Society for Imaging Informatics in Medicine 2019.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c474t-d8b89c573355eac7ef46322e980d4afe59dfaa219246c708967eb7a671bfbce3</citedby><cites>FETCH-LOGICAL-c474t-d8b89c573355eac7ef46322e980d4afe59dfaa219246c708967eb7a671bfbce3</cites><orcidid>0000-0003-2433-5676</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC7165212/pdf/$$EPDF$$P50$$Gpubmedcentral$$H</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC7165212/$$EHTML$$P50$$Gpubmedcentral$$H</linktohtml><link.rule.ids>230,314,723,776,780,881,27903,27904,53769,53771</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/31659588$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Sharma, Urvashi</creatorcontrib><creatorcontrib>Sood, Meenakshi</creatorcontrib><creatorcontrib>Puthooran, Emjee</creatorcontrib><title>A Block Adaptive Near-Lossless Compression Algorithm for Medical Image Sequences and Diagnostic Quality Assessment</title><title>Journal of digital imaging</title><addtitle>J Digit Imaging</addtitle><addtitle>J Digit Imaging</addtitle><description>The near-lossless compression technique has better compression ratio than lossless compression technique while maintaining a maximum error limit for each pixel. It takes the advantage of both the lossy and lossless compression methods providing high compression ratio, which can be used for medical images while preserving diagnostic information. The proposed algorithm uses a resolution and modality independent threshold-based predictor, optimal quantization (
q
) level, and adaptive block size encoding. The proposed method employs resolution independent gradient edge detector (RIGED) for removing inter-pixel redundancy and block adaptive arithmetic encoding (BAAE) is used after quantization to remove coding redundancy. Quantizer with an optimum
q
level is used to implement the proposed method for high compression efficiency and for the better quality of the recovered images. The proposed method is implemented on volumetric 8-bit and 16-bit standard medical images and also validated on real time 16-bit-depth images collected from government hospitals. The results show the proposed algorithm yields a high coding performance with BPP of 1.37 and produces high peak signal-to-noise ratio (PSNR) of 51.35 dB for 8-bit-depth image dataset as compared with other near-lossless compression. The average BPP values of 3.411 and 2.609 are obtained by the proposed technique for 16-bit standard medical image dataset and real-time medical dataset respectively with maintained image quality. The improved near-lossless predictive coding technique achieves high compression ratio without losing diagnostic information from the image.</description><subject>Adaptive algorithms</subject><subject>Algorithms</subject><subject>Coding</subject><subject>Compression</subject><subject>Compression ratio</subject><subject>Compression tests</subject><subject>Datasets</subject><subject>Diagnostic systems</subject><subject>Image compression</subject><subject>Image quality</subject><subject>Imaging</subject><subject>Measurement</subject><subject>Medical diagnosis</subject><subject>Medical imaging</subject><subject>Medicine</subject><subject>Medicine & Public Health</subject><subject>Noise levels</subject><subject>Optimization</subject><subject>Pixels</subject><subject>Quality assessment</subject><subject>Quality control</subject><subject>Radiology</subject><subject>Real time</subject><subject>Redundancy</subject><subject>Signal to noise 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Block Adaptive Near-Lossless Compression Algorithm for Medical Image Sequences and Diagnostic Quality Assessment</title><author>Sharma, Urvashi ; Sood, Meenakshi ; Puthooran, Emjee</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c474t-d8b89c573355eac7ef46322e980d4afe59dfaa219246c708967eb7a671bfbce3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Adaptive algorithms</topic><topic>Algorithms</topic><topic>Coding</topic><topic>Compression</topic><topic>Compression ratio</topic><topic>Compression tests</topic><topic>Datasets</topic><topic>Diagnostic systems</topic><topic>Image compression</topic><topic>Image quality</topic><topic>Imaging</topic><topic>Measurement</topic><topic>Medical diagnosis</topic><topic>Medical imaging</topic><topic>Medicine</topic><topic>Medicine & Public Health</topic><topic>Noise 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It takes the advantage of both the lossy and lossless compression methods providing high compression ratio, which can be used for medical images while preserving diagnostic information. The proposed algorithm uses a resolution and modality independent threshold-based predictor, optimal quantization (
q
) level, and adaptive block size encoding. The proposed method employs resolution independent gradient edge detector (RIGED) for removing inter-pixel redundancy and block adaptive arithmetic encoding (BAAE) is used after quantization to remove coding redundancy. Quantizer with an optimum
q
level is used to implement the proposed method for high compression efficiency and for the better quality of the recovered images. The proposed method is implemented on volumetric 8-bit and 16-bit standard medical images and also validated on real time 16-bit-depth images collected from government hospitals. The results show the proposed algorithm yields a high coding performance with BPP of 1.37 and produces high peak signal-to-noise ratio (PSNR) of 51.35 dB for 8-bit-depth image dataset as compared with other near-lossless compression. The average BPP values of 3.411 and 2.609 are obtained by the proposed technique for 16-bit standard medical image dataset and real-time medical dataset respectively with maintained image quality. The improved near-lossless predictive coding technique achieves high compression ratio without losing diagnostic information from the image.</abstract><cop>Cham</cop><pub>Springer International Publishing</pub><pmid>31659588</pmid><doi>10.1007/s10278-019-00283-3</doi><tpages>15</tpages><orcidid>https://orcid.org/0000-0003-2433-5676</orcidid><oa>free_for_read</oa></addata></record> |
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| source | Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; PubMed Central |
| subjects | Adaptive algorithms Algorithms Coding Compression Compression ratio Compression tests Datasets Diagnostic systems Image compression Image quality Imaging Measurement Medical diagnosis Medical imaging Medicine Medicine & Public Health Noise levels Optimization Pixels Quality assessment Quality control Radiology Real time Redundancy Signal to noise ratio |
| title | A Block Adaptive Near-Lossless Compression Algorithm for Medical Image Sequences and Diagnostic Quality Assessment |
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