Auto-shape Lossless Compression of Pharynx and Esophagus Fluoroscopic Images

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

doi:10.1007/s10916-015-0200-z

Cited by 4 publications

(1 citation statement)

References 13 publications

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“…Arif et al [ 16 ] introduced an effective approach for minimizing fluoroscopic imaging. The retrieved ROI of this approach is minimized through a grouping of run length as well as hepatic coding.…”

Section: Introductionmentioning

confidence: 99%

Optimal Medical Image Size Reduction Model Creation Using Recurrent Neural Network and GenPSOWVQ

Sridhar¹,

Pareek²,

Kalidoss

et al. 2022

Journal of Healthcare Engineering

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Medical diagnosis is always a time and a sensitive approach to proper medical treatment. Automation systems have been developed to improve these issues. In the process of automation, images are processed and sent to the remote brain for processing and decision making. It is noted that the image is written for compaction to reduce processing and computational costs. Images require large storage and transmission resources to perform their operations. A good strategy for pictures compression can help minimize these requirements. The question of compressing data on accuracy is always a challenge. Therefore, to optimize imaging, it is necessary to reduce inconsistencies in medical imaging. So this document introduces a new image compression scheme called the GenPSOWVQ method that uses a recurrent neural network with wavelet VQ. The codebook is built using a combination of fragments and genetic algorithms. The newly developed image compression model attains precise compression while maintaining image accuracy with lower computational costs when encoding clinical images. The proposed method was tested using real-time medical imaging using PSNR, MSE, SSIM, NMSE, SNR, and CR indicators. Experimental results show that the proposed GenPSOWVQ method yields higher PSNR SSIMM values for a given compression ratio than the existing methods. In addition, the proposed GenPSOWVQ method yields lower values of MSE, RMSE, and SNR for a given compression ratio than the existing methods.

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Section: Introductionmentioning

confidence: 99%