ROI Based Medical Image Compression for Telemedicine Application

Kaur, Manpreet; Wasson, Vikas

doi:10.1016/j.procs.2015.10.037

Cited by 91 publications

(55 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In the medical image compression community [2],many technique for compression used like Lossless JPEG (Joint Photographic Experts Group) and lossless Wavelet [7]. JPEG formats is adopted by the Digital Imaging and Communications in Medicine (DICOM) group in their widely file format, but in the last years the wavelet compression algorithm give more performances than JPEG codec [4. In spite of this, a many image compression researches examine the use of compression for applying to medical images.…”

Section: Related Workmentioning

confidence: 99%

“…However it provides poor performance in terms of bit rate. The compression ratio (TC) potential vary about 2 to 8 next the information content of the image and the method applied [2,3]. The origin of the preference of doctors for lossless versus lossy compression is, as we said, avoid medical errors associated with poor image reconstruction.…”

Section: Related Workmentioning

confidence: 99%

“…In other words, there is no border between Transform approach and structural approach. compression ratio is called the ratio between the sizes of the raw image and the compressed image [2].…”

Section: Related Workmentioning

confidence: 99%

“…The current trend is the increasing use of digitized medical images [1,2] . Most of modern techniques of medical imaging produce 3D data ( MRI, CT , ultrasound, positron emission tomography ) and even 4D ( functional MRI, 3D echocardiography dynamic).…”

Section: Introductionmentioning

confidence: 99%

“…However ,Medical imaging play an important role on the diagnosis of diseases and surgical planning. These mega data need efficient transmission and long-term storage and [2]. So it is necessary to use algorithms of compression images in order to reduce the amount of data to be stored and transmitted.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Implementation of Novel Medical Image Compression Using Artificial Intelligence

Alrababah¹,

Al-Marghirani²

2016

ijacsa

View full text Add to dashboard Cite

Abstract-The medical image processing process is one of the most important areas of research in medical applications in digitized medical information. A medical images have a large sizes. Since the coming of digital medical information, the important challenge is to care for the conduction and requirements of huge data, including medical images. Compression is considered as one of the necessary algorithm to explain this problem. A large amount of medical images must be compressed using lossless compression. This paper proposes a new medical image compression algorithm founded on lifting wavelet transform CDF 9/7 joined with SPIHT coding algorithm, this algorithm applied the lifting composition to confirm the benefit of the wavelet transform. To develop the proposed algorithm, the outcomes compared with other compression algorithm like JPEG codec. Experimental results proves that the anticipated algorithm is superior to another algorithm in both lossy and lossless compression for all medical images tested. The Wavelet-SPIHT algorithm provides PSNR very important values for MRI images.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Implementation of Novel Medical Image Compression Using Artificial Intelligence

Alrababah¹,

Al-Marghirani²

2016

ijacsa

View full text Add to dashboard Cite

show abstract

Efficiently compressing 3D medical images for teleinterventions via CNNs and anisotropic diffusion

et al. 2021

View full text Add to dashboard Cite

Purpose Efficient compression of images while preserving image quality has the potential to be a major enabler of effective remote clinical diagnosis and treatment, since poor Internet connection conditions are often the primary constraint in such services. This paper presents a framework for organ‐specific image compression for teleinterventions based on a deep learning approach and anisotropic diffusion filter. Methods The proposed method, deep learning and anisotropic diffusion (DLAD), uses a convolutional neural network architecture to extract a probability map for the organ of interest; this probability map guides an anisotropic diffusion filter that smooths the image except at the location of the organ of interest. Subsequently, a compression method, such as BZ2 and HEVC‐visually lossless, is applied to compress the image. We demonstrate the proposed method on three‐dimensional (3D) CT images acquired for radio frequency ablation (RFA) of liver lesions. We quantitatively evaluate the proposed method on 151 CT images using peak‐signal‐to‐noise ratio (normalPSNR), structural similarity (italicSSIM), and compression ratio (italicCR) metrics. Finally, we compare the assessments of two radiologists on the liver lesion detection and the liver lesion center annotation using 33 sets of the original images and the compressed images. Results The results show that the method can significantly improve italicCR of most well‐known compression methods. DLAD combined with HEVC‐visually lossless achieves the highest average italicCR of 6.45, which is 36% higher than that of the original HEVC and outperforms other state‐of‐the‐art lossless medical image compression methods. The means of italicPSNR and italicSSIM are 70 dB and 0.95, respectively. In addition, the compression effects do not statistically significantly affect the assessments of the radiologists on the liver lesion detection and the lesion center annotation. Conclusions We thus conclude that the method has a high potential to be applied in teleintervention applications.

show abstract

A Hybrid Compression Technique with Region of Interest for Medical Images in Wireless Sensor Networks

Pinki,

Kumar

2023

Studies in Computational Intelligence

View full text Add to dashboard Cite

ROI Based Medical Image Compression for Telemedicine Application

Cited by 91 publications

References 12 publications

Implementation of Novel Medical Image Compression Using Artificial Intelligence

Implementation of Novel Medical Image Compression Using Artificial Intelligence

Efficiently compressing 3D medical images for teleinterventions via CNNs and anisotropic diffusion

A Hybrid Compression Technique with Region of Interest for Medical Images in Wireless Sensor Networks

Contact Info

Product

Resources

About