ROI-based DICOM image compression for telemedicine

Bairagi, Vinayak K.; Sapkal, A. M.

doi:10.1007/s12046-013-0126-4

Cited by 40 publications

(19 citation statements)

References 14 publications

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“…The technology can be also further extended to long-term care and telemedicine use for patients in other distant places. In addition, the proposed clustering-based compression system can be investigated for a variety of assistive applications, such as long-term care and telecare, which is one of the recently popular applications (Bairagi and Sapkal, 2013;Shany et al, 2012). For example, it can be applied to transfer patients' information continuously by monitoring the specific symbols on the screens.…”

Section: Implications For Clinical Applications and Benefitsmentioning

confidence: 99%

Clustering-based compression connected to cloud databases in telemedicine and long-term care applications

Hsu

2017

Telematics and Informatics

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Section: Implications For Clinical Applications and Benefitsmentioning

confidence: 99%

Clustering-based compression connected to cloud databases in telemedicine and long-term care applications

Hsu

2017

Telematics and Informatics

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“…Algorithms which deliver lossless compression within the regions of interest (ROI) and lossy compression elsewhere in the image, might be the key to providing efficient and accurate image coding to the medical community. 29,30,31 Such algorithms are termed as near-lossless compression schemes. Another approach is to maximize the overall compression ratio by dividing the image into multiple ROI and then compressing each region separately with its own compression ratio, instead of transforming the whole image.…”

Section: Compressionmentioning

confidence: 99%

Collaborative Compressed I-cloud Medical Image Storage with Decompress Viewer

Arka

Chellappan

2014

Procedia Computer Science

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Healthcare collaborative approach is anticipated to be an appropriate solution for disease management structure in the growing global population. Efficient disease management structure needs deep analytical skills in making effective decisions based on medical data. The nature of medical data being huge in size has been categorized as big data. Big data management in a collaborative environment needs multiple technological integrations. In this paper we have proposed an independent cloud based collaborative medical image storage and mobile viewer assisted with effective compression and decompression technique with unique security structure design. The proposed design has considered deep technology exploitation to offer medical image access via mobile devices by considering all the current constraints in terms of storage, image clarity and security. The proposed architecture allows both patient and medical practioners to have a cost effective approach in disease management and treatment process. It also introduces healthcare analysts and practitioners to the advancements in the computing field to effectively handle and make inferences from voluminous and heterogeneous healthcare data. Due to the broad nature of the topic, our primary emphasis will be on introducing healthcare data repositories, challenges, and concepts in data science. Not much focus will be on describing the details of any particular techniques and/or solutions in image compression, security and the medical field in particular other than convenient data access opportunity framework.

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“…The author has termed the scheme as embedded zero tree wavelet algorithm which is completely motivated from progressive medical image compression technique. Study on similar direction was carried out by Bairagi et al [5], where the author has considered DICOM image and integer wavelet transform. The result was found with minimized distortion after applying compression technique on the non-cropped region of the complete medical images.…”

Section: Related Workmentioning

confidence: 99%

Computational Modelling of Image Coding using ROI based Medical Image Compression

Suma¹,

Sridhar²

2014

IJCA

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In the age of digital medical imaging communication and robotic transmission of real-time image for robot guided operations in constrained bandwidth is a challenging task. The issue of compression, in medical images, is the prime focus of this study. The study has aimed to perform an investigation on the frequently adopted region-of-interest scheme called as MAXSHIFT. The design principle of this standard encoding technique allows encoding and highly prioritizing only the region of interest and then emphasis on the background (nonregion of interest area). The system allows the deployment of multiple and randomly shaped region of interest within the medical images using randomized weights for emphasizing each element of ROI. Supported by the discussion on some of the prior research work, and how this study is motivated, the present manuscript illustrates the experimental phases of implementing MAXSHIFT on two dimensional medical images. In order to check the robustness of the algorithm, the performance parameters such as bit per pixel (BPP) and Signal to Noise Ratio (SNR) is being evaluated on enormous medical images.

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ROI-based DICOM image compression for telemedicine

Cited by 40 publications

References 14 publications

Clustering-based compression connected to cloud databases in telemedicine and long-term care applications

Clustering-based compression connected to cloud databases in telemedicine and long-term care applications

Collaborative Compressed I-cloud Medical Image Storage with Decompress Viewer

Computational Modelling of Image Coding using ROI based Medical Image Compression

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