Lossless volumetric colour medical image compression using block based encoding

Kesavamurthy, T.; Krishnan, Thiyagarajan

doi:10.1504/ijmei.2012.048386

Cited by 4 publications

(3 citation statements)

References 7 publications

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“…A simple color medical image compression using spatial domain related operations is block‐based Encoding (Kesavamurthy and Thiyagarajan, ). In the block‐based encoding, 24 bit color volumetric medical images are processed separately in each color planes of size 8 bits.…”

Section: Block‐based Encodingmentioning

confidence: 99%

Lossless color medical image compression using adaptive block‐based encoding for human computed tomographic images

Kesavamurthy

Krishnan

2013

Int J Imaging Syst Tech

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The advancement in medical imaging systems such as computed tomography (CT), magnetic resonance imaging (MRI), positron emitted tomography (PET), and computed radiography (CR) produces huge amount of volumetric images about various anatomical structure of human body. There exists a need for lossless compression of these images for storage and communication purposes. The major issue in medical image is the sequence of operations to be performed for compression and decompression should not degrade the original quality of the image, it should be compressed loss lessly. In this article, we proposed a lossless method of volumetric medical image compression and decompression using adaptive block‐based encoding technique. The algorithm is tested for different sets of CT color images using Matlab. The Digital Imaging and Communications in Medicine (DICOM) images are compressed using the proposed algorithm and stored as DICOM formatted images. The inverse process of adaptive block‐based algorithm is used to reconstruct the original image information loss lessly from the compressed DICOM files. We present the simulation results for large set of human color CT images to produce a comparative analysis of the proposed methodology with block‐based compression, and JPEG2000 lossless image compression technique. This article finally proves the proposed methodology gives better compression ratio than block‐based coding and computationally better than JPEG 2000 coding. © 2013 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 23, 227–234, 2013

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Section: Block‐based Encodingmentioning

confidence: 99%

Lossless color medical image compression using adaptive block‐based encoding for human computed tomographic images

Kesavamurthy

Krishnan

2013

Int J Imaging Syst Tech

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“…T. K esavamurthy et al [9] have proposed AT-SPIHT algorithm based volumetric color medical image compression for the Picture Archiving and Communication Systems (PACS). Doctors have been enabled to employ the PACS in a better way for diagnosis and management in a short duration because by combining the RIS, HIS and the entire imaging modalities and scanning devices to it, it has stuck to the medical information system.…”

Section: Review Of Related Researchmentioning

confidence: 99%

ROI Based Encoding of Medical Images: An Effective Scheme Using Lifting Wavelets and SPIHT for Telemedicine

Rajkumar¹,

Latte²

2011

IJCTE

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Telemedicine characterized by transmission of medical data and images between users is one of the emerging fields in medicine. Huge bandwidth is necessary for transmitting medical images over the internet. Resolution factor and number of images per diagnosis makes even the size of the images that belongs to a single patient to be very large in size. So there is an immense need for efficient compression techniques for use in compressing these medical images. Each of the regions that are considered to be more important than others in medical images is termed as a Region of Interest (ROI) e.g. tumor region of the brain MRI. Thus, the regions of interest can be coded with high spatial resolution than the background while transmitting the images. By this, ROI of high compression rate and high quality can be obtained. This paper reviews the application of ROI coding in the field of telemedicine. Wavelet transform with lifting is used to perform image coding based on Set Partitioning in Hierarchical Trees (SPIHT). ROI coding with high spatial resolution than the background is accomplished using tiling method. High compression ratio is achieved by obtaining the ROI through user interaction and coding with the user given resolution. The experimental result shows that the application of ROI coding achieves high compression rate and quality ROI by using wavelet with lifting and tiling method

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“…Compared with the Huffman and DWT‐Huffman, a hybrid lossless image coding method provides maximum CR. Kesavamurthy and Thiyagarajan 21 proposed a lossless method of image coding and decoding using a block‐based technique. Different datasets of CT color images are utilized to test the algorithm and to recover the original image losslessly, the inverse algorithm is used.…”

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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Lossless volumetric colour medical image compression using block based encoding

Cited by 4 publications

References 7 publications

Lossless color medical image compression using adaptive block‐based encoding for human computed tomographic images

Lossless color medical image compression using adaptive block‐based encoding for human computed tomographic images

ROI Based Encoding of Medical Images: An Effective Scheme Using Lifting Wavelets and SPIHT for Telemedicine

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

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