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...
| Publicado en: | Journal of Digital Imaging Vol. 33; no. 2; pp. 516 - 531 |
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| Autores principales: | , , |
| Formato: | algorithm diagnostic images equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Apr2020
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=142764021&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 142764021 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Apr2020 vid: 33 iid: 2 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 142764021 142764021 142764021 10.1007/s10278-019-00283-3 142764021 ppf: 516 ppct: 15 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: A Block Adaptive Near-Lossless Compression Algorithm for Medical Image Sequences and Diagnostic Quality Assessment. aug: au: Sharma, Urvashi Sood, Meenakshi Puthooran, Emjee affil: Department of Electronics and Communication, Jaypee University of Information Technology, Waknaghat, H.P, India sug: subj: Algorithms Image Enhancement Human Coding Hospitals, Public ab: 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. pubtype: Academic Journal doctype: algorithm diagnostic images equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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