Lossless Compression of Medical Image Sequences Using a Resolution Independent Predictor and Block Adaptive Encoding

Sharma, Urvashi; Sood, Meenakshi; Puthooran, Emjee

doi:10.32985/ijeces.9.2.4

Cited by 7 publications

(5 citation statements)

References 21 publications

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“…For intra‐slice prediction, we use RIGED prediction, 32 which is independent of image resolution and modality.…”

Section: Proposed Methodology For 3d Medical Image Datasetsmentioning

confidence: 99%

“…Residual image resulting from RIGED has minimum entropy value that requires lesser number of bits for encoding 32,33 . RIGED provided an optimal threshold value for better prediction and removes inter‐pixel redundancy from the image.…”

Section: Proposed Methodology For 3d Medical Image Datasetsmentioning

confidence: 99%

“…Optimal block size of 8 × 8 is previously given by authors after empirical analysis on block size ranging from 4 × 4 to 128 × 128 31,32 . BPP values are calculated at varying block sizes for all the dataset given in Section 2.…”

Section: Proposed Methodology For 3d Medical Image Datasetsmentioning

confidence: 99%

“…The RIGED is resolution and modality independent but it depends upon image bit depth. Sharma et al 31,32 proposed a block‐based lossless coding technique for volumetric medical image coding of 8‐bit depth and higher bit depth up‐to 16‐bit depth. This article provides the optimal block size for encoding that is independent of image resolution and modality used after prediction in predictive coding technique.…”

Section: Introductionmentioning

confidence: 99%

“…A combination of inter‐slice and intra‐slice pixel value prediction, namely inter‐slice correlation switched predictor (ICSP) is proposed in this work to remove both inter‐slice and intra‐slice redundancies from the volumetric image as there are highly correlated image slices in volumetric image data. Existing RIGED employed for 2D prediction 30,32 is extended for 3D prediction to exploit inter‐slice correlation. Use of RIGED, which is modality and resolution independent, brings the novelty and improved performance for 3D prediction of volumetric images.…”

Section: Introductionmentioning

confidence: 99%

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Volumetric medical image compression using inter‐slice correlation switched prediction approach

Sharma

Sood²,

Puthooran³

2020

Int J Imaging Syst Tech

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With the advancement in medical data acquisition and telemedicine systems, image compression has become an important tool for image handling, as the tremendous amount of data generated in medical field needs to be stored and transmitted effectively. Volumetric MRI and CT images comprise a set of image slices that are correlated to each other. The prediction of the pixels in a slice depends not only upon the spatial information of the slice, but also the inter‐slice information to achieve compression. This article proposes an inter‐slice correlation switched predictor (ICSP) with block adaptive arithmetic encoding (BAAE) technique for 3D medical image data. The proposed ICSP exploits both inter‐slice and intra‐slice redundancies from the volumetric images efficiently. Novelty of the proposed technique is in selecting the correlation coefficient threshold (Tϒ) for switching of ICSP. Resolution independent gradient edge detector (RIGED) at optimal prediction threshold value is proposed for intra‐slice prediction. Use of RIGED, which is modality and resolution independent, brings the novelty and improved performance for 3D prediction of volumetric images. BAAE is employed for encoding of prediction error image to resulting in higher compression efficiency. The proposed technique is also extended for higher bit depth volumetric medical images (16‐bit depth) presenting significant compression gain of 3D images. The performance of the proposed technique is compared with the state‐of‐the art techniques in terms of bits per pixel (BPP) for 8‐bit depth and was found to be 31.21%, 27.55%, 21.89%, and 2.39% better than the JPEG‐2000, CALIC, JPEG‐LS, M‐CALIC, and 3D‐CALIC respectively. The proposed technique is 11.86%, 8.56%, 7.97%, 6.80%, and 4.86% better than the M‐CALIC, 3D CALIC, JPEG‐2000, JPEG‐LS and CALIC respectively for 16‐bit depth image datasets. The average value of compression ratio for 8‐bit and 16‐bit image dataset is obtained as 3.70 and 3.11 respectively by the proposed technique.

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“…For intra‐slice prediction, we use RIGED prediction, 32 which is independent of image resolution and modality.…”

Section: Proposed Methodology For 3d Medical Image Datasetsmentioning

confidence: 99%

Section: Proposed Methodology For 3d Medical Image Datasetsmentioning

confidence: 99%

Section: Proposed Methodology For 3d Medical Image Datasetsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%