Coronary angiogram video compression using wavelet-based contourlet transform and region-of-interest technique

Selvi, G. Uma Vetri; Nadarajan, R.

doi:10.1049/iet-ipr.2011.0284

Cited by 8 publications

(1 citation statement)

References 12 publications

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“…for diagnosis is referred to as region of interest (Selvi and Nadarajan, 2012). The main challenge in compressing medical images is to compress the ROI region without any loss of important information.…”

Section: Jcsmentioning

confidence: 99%

Medical Image Compression Technique Using Listless Set Partitioning in Hierarchical Trees and Contextual Vector Quantization for Brain Images

Sridevi¹

2013

Journal of Computer Science

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A hybrid image compression techniques has been developed to compress medical images. Due to the extensive use of medical images like CT and MR scan, these medical imagery are stored for a longer period for the continuous monitoring of the patients and the amount of data associated with images is large and it occupies enormous storage capacity. So, the medical images need to be compressed to reduce the storage cost and for transmission without any loss. In this study, a hybrid method which combines the Listless Set Partitioning in Hierarchical Trees (LSPIHT) and the Contextual Vector Quantization (CVQ) method for the compression of brain images. Here, the region containing the most important information for diagnosis is called Region of Interest (ROI) and this is to be compressed with out any loss in the quality. In this method, the ROI is encoded separately using LSPIHT and the Back Ground region (BG) is encoded using CVQ. Finally, the two regions are merged together to get the reconstructed image. Our results show that the proposed method gives very good image quality for diagnosis without any degradable loss. The performance of the compression technique is evaluated using the parameters (CR, MSE, PSNR) and achieved better result compared to other existing methods. As a result, we strongly believe that using our method, we can overcome the limitations in storage and transmission of medical images that are produced day by day.

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Section: Jcsmentioning

confidence: 99%

Medical Image Compression Technique Using Listless Set Partitioning in Hierarchical Trees and Contextual Vector Quantization for Brain Images

Sridevi¹

2013

Journal of Computer Science

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A High Secure Speech Transmission Using Audio Steganography and Duffing Oscillator

Hameed

2021

Wireless Pers Commun

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Diagnostically lossless coding of X-ray angiography images based on background suppression

Bartrina-Rapesta

Blanes

et al. 2016

Computers & Electrical Engineering

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X-ray angiography images are widely used to identify irregularities in the vascular system. Because of their high spatial resolution and the large amount of images generated daily, coding of X-ray angiography images is becoming essential. This paper proposes a diagnostically lossless coding method based on automatic segmentation of the focal area using ray-casting and α-shapes. The diagnostically relevant Region of Interest is first identified by exploiting the inherent symmetrical features of the image. The background is then suppressed and the resulting images are encoded using lossless and progressive lossy-to-lossless methods, including JPEG-LS, JPEG2000, H.264 and HEVC. Experiments on a large set of X-ray angiography images suggest that our method correctly identifies the Region of Interest. When compared to the case of coding with no background suppression, the method achieves average bit-stream reductions of nearly 34% and improvements on the reconstruction quality of up to 20 dB-SNR for progressive decoding.

show abstract

Coronary angiogram video compression using wavelet-based contourlet transform and region-of-interest technique

Cited by 8 publications

References 12 publications

Medical Image Compression Technique Using Listless Set Partitioning in Hierarchical Trees and Contextual Vector Quantization for Brain Images

Medical Image Compression Technique Using Listless Set Partitioning in Hierarchical Trees and Contextual Vector Quantization for Brain Images

A High Secure Speech Transmission Using Audio Steganography and Duffing Oscillator

Diagnostically lossless coding of X-ray angiography images based on background suppression

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