Region based lossless compression for digital images in telemedicine application

Brindha, B.; Raghuraman, G.

doi:10.1109/iccsp.2013.6577112

Cited by 9 publications

(3 citation statements)

References 11 publications

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“…The tumor is located over very small regions, nearly about 5-10% of the total area of the image. The rest of the image is termed as NON-ROI [16]. Hybrid compression combines lossy and lossless techniques to achieve higher compression at visually acceptable quality.…”

Section: Proposed Methodsmentioning

confidence: 99%

Performance Analysis of Region based Hybrid Compression for Medical Images

Joshi¹,

Rawat²

2016

IJCA

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The high quality of the image produced by CT scan and MRI techniques is required to be maintained in order to avoid wrong diagnosis along with reduced file size. A lossless compression technique retains the image quality but achieves low to moderate compression ratio. Lossy compression, on the other hand, provides higher compression at the cost of degraded image quality. Thus there is need of intermediate method that can satisfy both the requirements. One such approach is the region based hybrid compression in which both lossless and lossy techniques are integrated to obtained better results. Present work comprises region based hybrid compression using Huffman coding and SPIHT. First the diagnostically important region is separated from the rest of the image by a segmentation procedure. The extracted ROI is coded using lossless Huffman coding and SPIHT compression is used for rest of the image also called as background. Performance of the proposed method is evaluated in terms of full reference and no reference parameters.

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Section: Proposed Methodsmentioning

confidence: 99%

Performance Analysis of Region based Hybrid Compression for Medical Images

Joshi¹,

Rawat²

2016

IJCA

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“…The compression ratio of medical images, as well as generalpurpose images, is improved. Similarly, several schemes have been proposed in the last decade for the lossless compression of medical images [39,40,41].…”

Section: Literature Surveymentioning

confidence: 99%

Optimal Compression of Medical Images

Habib¹

2019

IJACSA

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In today's healthcare system, medical images are playing a vital role in the diagnosis. The challenges arise to the hospital management systems (HMS) are to store and communicate the large volume of medical images generated by various imaging modalities. Efficient compression of medical images is required to reduce the bit rate to increase the storage capacity and speed-up the transmission without affecting its quality. Over the past few decades, several compression standards have been proposed. In this paper, an intelligent JPEG2000 compression scheme is presented to compress the medical images efficiently. Unlike the traditional compression techniques, genetic programming (GP)-based quantization matrices are used to quantize the wavelet coefficients of the input image. Experimental results validate the usefulness of the proposed intelligent compression scheme.

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“…All regions of medical image do not have equal importance as only some portions are diagnostically important. In such cases, these regions require to be encoded at a higher superiority than the background, lesser important regions [4]. In medical imaging data loss in the diagnostically important regions (ROI) is not affordable.…”

Section: Medical Imagingmentioning

confidence: 99%

Review: ROI Based Medical Image Compression using DCT and HAAR Wavelet

Singh¹,

Singh²

2015

International Journal of Advanced Research in Computer and Comm

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Image compression addresses the problem of reducing the amount of data required to represent a digital image. There are some areas of medicine where it is sufficient to maintain high image quality only for diagnostically significant regions, for example, tumor section of the brain MRI. This paper represents a compression method based on ROI and its performance analysis focusing on Discrete Cosine Transform technique. It is used for reducing the redundancy that is nothing but avoiding the replica data. The DCT is a mathematical function that transforms digital image data from the spatial domain to the frequency domain.

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Region based lossless compression for digital images in telemedicine application

Cited by 9 publications

References 11 publications

Performance Analysis of Region based Hybrid Compression for Medical Images

Performance Analysis of Region based Hybrid Compression for Medical Images

Optimal Compression of Medical Images

Review: ROI Based Medical Image Compression using DCT and HAAR Wavelet

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