Fast Compression Algorithms for Capsule Endoscope Images

Li, Jing; Deng, Youjun

doi:10.1109/cisp.2009.5300914

Cited by 11 publications

(5 citation statements)

References 8 publications

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“…The compression ratio (CR) is calculated by (3). DWT compresses the Y image along with with the sub sampling of U and V components gives CR of 79.16%.…”

Section: Results and Performance Analysismentioning

confidence: 99%

Hardware efficient implementation of video capsule endoscopy

Darji

Nijin

2014

2014 IEEE 3rd Global Conference on Consumer Electronics (GCCE)

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In this paper, we propose hardware efficient implementation of lifting based 2-D DWT and Sobel based edge detector for detail diagnosis in Wireless capsule endoscopy (WCE) in real-time. Architecture is developed based on the properties of the endoscopic images and the CMOS sensors. Proposed edge detection scheme works on the compressed DWT coefficients and offers 83.1 % compression ratio (CR). VLSI implementation of the proposed scheme consumes only 17050 standard cells.

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“…The compression ratio (CR) is calculated by (3). DWT compresses the Y image along with with the sub sampling of U and V components gives CR of 79.16%.…”

Section: Results and Performance Analysismentioning

confidence: 99%

Hardware efficient implementation of video capsule endoscopy

Darji

Nijin

2014

2014 IEEE 3rd Global Conference on Consumer Electronics (GCCE)

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“…In Table 7 , the proposed compressor is compared with other related compression algorithms. Here, we see that the proposed compressor outperforms in CR and in image quality when compared with [ 14 , 24 ]. The works in [ 8 – 10 , 12 , 13 , 15 ] are lossy compressors; these algorithms produces distortions in the reconstructed images which may lead to inaccurate diagnostics.…”

Section: The Lossless Image Compressormentioning

confidence: 88%

Design of a Lossless Image Compression System for Video Capsule Endoscopy and Its Performance in In-Vivo Trials

Khan

Wahid

2014

Sensors

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In this paper, a new low complexity and lossless image compression system for capsule endoscopy (CE) is presented. The compressor consists of a low-cost YEF color space converter and variable-length predictive with a combination of Golomb-Rice and unary encoding. All these components have been heavily optimized for low-power and low-cost and lossless in nature. As a result, the entire compression system does not incur any loss of image information. Unlike transform based algorithms, the compressor can be interfaced with commercial image sensors which send pixel data in raster-scan fashion that eliminates the need of having large buffer memory. The compression algorithm is capable to work with white light imaging (WLI) and narrow band imaging (NBI) with average compression ratio of 78% and 84% respectively. Finally, a complete capsule endoscopy system is developed on a single, low-power, 65-nm field programmable gate arrays (FPGA) chip. The prototype is developed using circular PCBs having a diameter of 16 mm. Several in-vivo and ex-vivo trials using pig's intestine have been conducted using the prototype to validate the performance of the proposed lossless compression algorithm. The results show that, compared with all other existing works, the proposed algorithm offers a solution to wireless capsule endoscopy with lossless and yet acceptable level of compression.

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“…The prior works under lossy compression are based on discrete cosine transform (DCT) [7], [8], discrete wavelet transform (DWT) [9] and compressed sensing [10]. Low quality reconstruction and complex computation are its major drawbacks.…”

Section: Performance Of Predictive Coders For Wireless Capsule Endoscmentioning

confidence: 99%

“…The performance of the compression scheme is evaluated using namely compression rate (CR) and peak signal to noise ratio (PSNR). The compression rate is defined as the ratio of the total bits after compression to total bits before compression as given in (7) and it is expressed in percentage [5]. The total bits before compression is obtained by multiplying the height, width, and number of bits used to represent each pixel in the original input image.…”

Section: Performance Measuresmentioning

confidence: 99%

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Performance of Predictive Coders for Wireless Capsule Endoscopy Image Compression

Babu*,

Chandy*

2020

IJRTE

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Wireless capsule endoscopy is a medical diagnostic technique developed for the endoscopic examination of the small bowel. The encoder module is the core of the wireless capsule endoscopic system impacting on power and area requirement for the hardware implementation of the capsule. One of the remarkable features of the endoscopic image is that the neighboring pixels are highly correlated. Two predictive coding techniques are considered in this work exploiting the above fact. The first predictive coder i.e., DPCM coder is based on previous horizontal neighboring pixel, whereas the second predictive coder is based on adjacent horizontal and diagonal neighbors. The performance of the predictive coders is tested with 41 small bowel type endoscopic images available in the Gastrolab dataset. The results show that the average compression rate and peak signal to noise ratio attained by DPCM coder and newly tested predictive coder are 66.37 % & 73.03 % and 32.17 dB & 35.55 dB, respectively

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Fast Compression Algorithms for Capsule Endoscope Images

Cited by 11 publications

References 8 publications

Hardware efficient implementation of video capsule endoscopy

Hardware efficient implementation of video capsule endoscopy

Design of a Lossless Image Compression System for Video Capsule Endoscopy and Its Performance in In-Vivo Trials

Performance of Predictive Coders for Wireless Capsule Endoscopy Image Compression

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