Binary medical image compression using the volumetric run-length approach

Aldemir, Erdoğan; Tohumoğlu, Gülay; Selver, M. Alper

doi:10.1080/13682199.2019.1565695

Cited by 11 publications

(10 citation statements)

References 22 publications

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“…20,21 The characteristics of the bi-level (segmented medical data) differ from natural image data in terms of entropy level, compactness, 22 and the energy of the image matrix, which is defined as an absolute value of enclosed curve or volume, and morphological structure. 23 And thus, the compression techniques that are not specifically designed for medical data cannot completely reveal the corresponding redundancy. In this study, the 3D-RLE method, which taking account of the coherence with the morphology of the organs and scanning procedure to reveal redundancy, is suggested for telemedicine networks.…”

Section: B Compression Methodsmentioning

confidence: 99%

“…3. 23 Furthermore, since the boundary of the compression rate is determined by the entropy of the data, the algorithm seeks out a low-entropy form of the image by scanning the matrix coherent with the shape of the organs to achieve high compression ratios. The method provides a morphological coherence by appropriate scanning procedure to decrease the entropy of the image.…”

Section: B Compression Methodsmentioning

confidence: 99%

“…In general, generally modern compression standards are not context‐based regarding bi‐level segmented medical images and the irreversible techniques are not preferred for medical image compression . The characteristics of the bi‐level (segmented medical data) differ from natural image data in terms of entropy level, compactness, and the energy of the image matrix, which is defined as an absolute value of enclosed curve or volume, and morphological structure . And thus, the compression techniques that are not specifically designed for medical data cannot completely reveal the corresponding redundancy.…”

Section: Methodsmentioning

confidence: 99%

“…The RLE is designed specifically for bi‐level medical data and it utilizes inter‐slice correlation through a low‐computational volumetric algorithm, see in Fig. . Furthermore, since the boundary of the compression rate is determined by the entropy of the data, the algorithm seeks out a low‐entropy form of the image by scanning the matrix coherent with the shape of the organs to achieve high compression ratios.…”

Section: Methodsmentioning

confidence: 99%

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Reversible 3D compression of segmented medical volumes: usability analysis for teleradiology and storage

et al. 2020

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Background: DICOM standard does not have modules that provide the possibilities of two-dimensional Presentation States to three-dimensional (3D). Once the final 3D rendering is obtained, only video/image exporting or snapshots can be used. To increase the utility of 3D Presentation States in clinical practice and teleradiology, the storing and transferring the segmentation results, obtained after tedious procedures, can be very effective. Purpose: To propose a strategy for preserving interaction and mobility of visualizations for teleradiology by storing and transferring only binary segmented data, which is effectively compressed by modern adaptive and context-based reversible methods. Material and Methods: A diverse set of segmented data, which include four abdominal organs (liver, spleen, right, and left kidneys) from 20 T1-DUAL and 20 T2-SPIR MRI, liver from 20 CT, and abdominal aorta with aneurysms (AAA) from 19 computed tomography-angiography datasets, are collected. Each organ is segmented manually by expert physicians, and binary volumes are created. The well-established reversible binary compression methods PNG, JPEG-LS, JPEG-XR, CCITT-G4, LZW, JBIG2, and ZIP are applied to medical datasets. Recently proposed context-based (3D-RLE) and adaptive (ABIC) algorithms are also employed. The performance assessment has been presented in terms of the compression ratio that is a universal compression metric. Results: Reversible compression of binary volumes results with substantial decreases in file size such as 254 to 2.14 MB for CT-AAA, 56.7 to 0.3 MB for CT-liver. Moreover, compared to the performance of well-established methods (i.e., mean 76.14%), CR is observed to be increased significantly for all segmented organs from both CT and MRI datasets when ABIC (95.49%) and 3D-RLE (94.98%) are utilized. The hypothesis is that morphological coherence of scanning procedure and adaptation between the segmented organs, that is, bi-level images, contributes to compression performance. Although the performance of well-established techniques is satisfactory, the sensitivity of ABIC to modality type and the advantage of 3D-RLE when the spatial coherence between the adjacent slices are high results with up to 10 times more CR performance. Conclusion: Adaptive and context-based compression strategies allow effective storage and transfer of segmented binary data, which can be used to re-produce visualizations for better teleradiology practices preserving all interaction mechanisms.

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Section: B Compression Methodsmentioning

confidence: 99%

Section: B Compression Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Reversible 3D compression of segmented medical volumes: usability analysis for teleradiology and storage

et al. 2020

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“…It utilize 3D predictors to exploit inter‐slice correlation and able to compress volumetric dataset. Aldemir et al 25 developed an algorithm for three‐dimensional (3D) binary medical data that take advantage of run‐length algorithm. The 3D‐RLE algorithm is different from 2D approach and designed to compress the volumetric data by employing inter‐slice correlation between the voxels.…”

Section: Introductionmentioning

confidence: 99%

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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Improved near-lossless technique using the Huffman coding for enhancing the quality of image compression

Otair

Abualigah

Qawaqzeh

2022

Multimed Tools Appl

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Binary medical image compression using the volumetric run-length approach

Cited by 11 publications

References 22 publications

Reversible 3D compression of segmented medical volumes: usability analysis for teleradiology and storage

Reversible 3D compression of segmented medical volumes: usability analysis for teleradiology and storage

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

Improved near-lossless technique using the Huffman coding for enhancing the quality of image compression

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