Lossless compression of Fluoroscopy medical images using correlation and the combination of Run-length and Huffman coding

Arif, Arif Sameh; Mansor, Sarina; Karim, Hezrul Abdul; Logeswaran, Rajasvaran

doi:10.1109/iecbes.2012.6498149

Cited by 10 publications

(3 citation statements)

References 9 publications

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“…This new method is based on adding the Huffman tables to the JPEG header that can "be optimised on a per image basis". However, Arif (2012) developed a different technique to compress medical images by combining two effective methods, namely Huffman coding and run-length coding, both of which are lossless compression methods, to reduce the delivery time and the data volume, save the power in scanning applications, and achieve maximum compression. Jimenez-Rodriguez (2013) developed a new method called the visual lossless coding, which is based on the ability to recognise the important parts of the image, which are compressed by using JPEG2000.…”

Section: Lossless Compression Methodsmentioning

confidence: 99%

Lossless Compression Methods for Real-Time Images

Mohammed

Silfhout

2020

EAJSE

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This paper proposes and implements two lossless methods, to compress real-time greyscale medical images, which are Huffman coding and a new lossless method called Reduced Lossless Compression Method (RLCM), both of which were tested when applying a random sample of greyscale medical images with a size of 256x256 pixels. Different factors were measured to check the compression method performances such as the compression time, the compressed image size, and the compression ratio (CR). The system is fully implemented on a field programmable gate array (FPGA) using a fully hardware based (no software driven processor) system architecture. A Terasic DE4 board was used as the main platform for implementing and testing the system using Quartus-II software and tools for design and debugging. The impact of compressing the image and carrying the compressed data through parallel lines is like the impact of compressed the same image inside a single core with a higher compression ratio, in this system between 7.5 and 126.8.

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Section: Lossless Compression Methodsmentioning

confidence: 99%

Lossless Compression Methods for Real-Time Images

Mohammed

Silfhout

2020

EAJSE

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“…In our recent work, we have performed lossless compression of Fluoroscopy medical images using correlation and Huffman coding (HM-D) [10]. The proposed method achieved compression ratio of 7.97.…”

Section: Introductionmentioning

confidence: 99%

“…Specifically, we propose a new framework for lossless medical image compression based on classifying the images depending on correlation and coding the difference between sequential images using the combination of Run Length and Huffman coding. The extended method achieved compression ratio of 11.41 [11]. In this paper, we expand the work to enhance the compression ratio by testing the two sequential images to examine the shifting existence.…”

Section: Introductionmentioning

confidence: 99%

Lossless Compression of Pharynx and Esophagus in Fluoroscopic Medical Images

Arif¹,

Mansor²,

Logeswaran³

et al. 2013

IJBBB

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Abstract-Hospitals and medical centers produce a tremendous amount of sequential images for medical examinations such as MRI, CT and Fluoroscopy. This series of images takes up a large amount of storage space, in addition to the cost and time incurred during transmission. For medical data, lossless compression is preferable to the greater gains of lossy compression, in the interest of reliability. This paper proposes a new method for lossless compression of pharynx and esophagus fluoroscopy images, depending on correlation and combination of Run Length and Huffman. Otherwise, the shifted images moved to a shifted group and compress separately. From the experimental results obtained, the proposed method achieved improved performance with a compression ratio of 12.2 for the proposed combination of Run-length and Huffman coding (R. Huff) on the difference images as compared to 1.35 for the standard method.

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Auto-shape Lossless Compression of Pharynx and Esophagus Fluoroscopic Images

et al. 2015

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The massive number of medical images produced by fluoroscopic and other conventional diagnostic imaging devices demand a considerable amount of space for data storage. This paper proposes an effective method for lossless compression of fluoroscopic images. The main contribution in this paper is the extraction of the regions of interest (ROI) in fluoroscopic images using appropriate shapes. The extracted ROI is then effectively compressed using customized correlation and the combination of Run Length and Huffman coding, to increase compression ratio. The experimental results achieved show that the proposed method is able to improve the compression ratio by 400 % as compared to that of traditional methods.

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Lossless compression of Fluoroscopy medical images using correlation and the combination of Run-length and Huffman coding

Cited by 10 publications

References 9 publications

Lossless Compression Methods for Real-Time Images

Lossless Compression Methods for Real-Time Images

Lossless Compression of Pharynx and Esophagus in Fluoroscopic Medical Images

Auto-shape Lossless Compression of Pharynx and Esophagus Fluoroscopic Images

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