In medical imaging, denoising is very important for analysis of images, diagnosis and treatment of diseases. Currently, image denoising methods based on deep learning are effective, where the methods are however limited for the requirement of training sample size (i.e., not successful enough for small data size). Using small sample size, we design deep feed forward denoising convolutional neural networks by studying the model in deep framework, learning approach and regularization approach for medical image denoising. More specifically, we use residual learning as a learning approach and batch normalization as regularization in the deep model. Unlike most of the other image denoising approaches which directly learn the latent clean images, the residual learning approach learns the noise from the noisy images instead of the latent clean images where the denoised images are obtained by subtracting the learned residual from the noisy image. Moreover, batch normalization is integrated with residual learning to improve model learning accuracy and training time. We compute the quality of the reconstructed or denoised image in standard image quality metrics, peak signal to noise ratio and structural similarity and compare our model performance with some medical image denoising techniques. Experimental results reveal that our approach has better performance than some other methods.

Medical image denoising using convolutional neural network: a residual learning approach

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

Wireless multimedia sensor network (WMSN) comprising of miniature sensor nodes is capable of processing multimedia data traffic such as still images and video from the environment. There is a wide range of applications which get benefited from such network. Unprocessed multimedia transmission is always expensive in terms of processing power, storage, and bandwidth. So, data processing is a challenge in WMSN. Exploring low-overhead data compression technique is a solution towards this problem. In this work we propose an energy saving image compression technique for WMSN using curve fitting technique considering the application of post-disaster situation analysis through image capturing of the affected area. Upon employing the method on the macroblocks of sensory image, curve fitting coefficients are generated and transmitted towards the sink thereby saves energy by transmitting reduced volume of data. Finally the design feasibility along with simulation results including statistical analysis is presented to evaluate efficacy of the scheme in terms of two conflicting parameters viz. energy consumption and peak signal to noise ratio. The comparative results confirm our scheme’s supremacy in WMSN application domain over existing methods.

An energy efficient image compression scheme for wireless multimedia sensor network using curve fitting technique

Versatile Approaches for Medical Image Compression: A Review

Medical centers produce a massive amount of sequential medical images for examinations such as CT, MRI and Fluoroscopy, where each examination of a patient consists of a series of images. This takes up a large amount of storage space, in addition to the cost and time incurred during transmission. For medical data, lossless compression is preferred to the greater gains of lossy compression, in the interest of accuracy. This paper proposes a new method for lossless compression of pharynx and esophagus fluoroscopy images, using correlation and combination of Run Length and Huffman coding on the difference pairs of images classified by correlation. From the experimental results obtained, the proposed method achieved improved performance with a compression ratio of 11.41 for the proposed combination of Run-length and Huffman coding (RLHM-D) on the difference images as compared to 1.31 for the standard images.

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

Coronavirus Disease 19 or well known as Covid-19 is infectious disease that attack human respirator system. This virus is contagious due to its fast transmission from small droplets that come to people that sneezing, coughing, or even talking. This virus can stay in air for long time and it also can survive on the inanimate surface things. During this time, some places like hospital, mall, and station are places where crowds gathered. People in those places have possibility to spread the virus not only through close contact but also due to touching the infected surfaces. That is why device that able to clean the infected surfaces needed. The Internet of Things based robots may give big impact in combating the coronavirus that stay on inanimate surfaces. The proposed system is the robot that able to disinfect the surfaces of things using UV-C lights. The implementation of UV disinfectant robot will indeed help health authorities in reducing the transmission.

2019 Novel Coronavirus Disease (Covid-19): Toward a Novel Design for Disinfection Robot to Combat Coronavirus (Covid-19) Using IoT Based Technology

Image de-noising is a core operation in image processing and computer vision. In this paper, combination of two popular methods in image de-noising bilateral and anisotropic-diffusion filtering is investigated to reduce the noise in medical images, while preserving the clarity of images. The proposed method experimented on 23 MRI images. The results obtained from the proposed method gained higher peak signal to noise ratio (PSNR) and compression ratio (CR) performance in comparison with the traditional methods by more than 8%.

Combined bilateral and anisotropic-diffusion filters for medical image de-noising

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.

Auto-shape Lossless Compression of Pharynx and Esophagus Fluoroscopic Images

Medical institutions generate an enormous amount of medical images for examinations such as fluoroscopy, where each examination of a patient consists of a collection of images. This takes up a large amount of valuable storage space, in addition to the amount of time and cost incurred during transmission. Although lossy compression provides for better compression, lossless compression is usually required and expected for medical diagnosis. This paper proposes a new method for a lossless compression on oesophagus fluoroscopy images using correlation. The differences of pairs or sequence of images are classified based on correlation. From the experimental results obtained, the proposed method achieved improved performance with a compression ratio of 7.97 as compared to standard Huffman coding (HM) loss less compression.

https://www.crc.gov.my/wp-content/uploads/journals/037_Lossless_compression_of_fluoroscopy_medical_images_using_correlation.pdf

Arif Sameh Arif

Papers

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

2019 Novel Coronavirus Disease (Covid-19): Toward a Novel Design for Disinfection Robot to Combat Coronavirus (Covid-19) Using IoT Based Technology

Combined bilateral and anisotropic-diffusion filters for medical image de-noising

Auto-shape Lossless Compression of Pharynx and Esophagus Fluoroscopic Images

Lossless Compression of Fluoroscopy Medical Images Using Correlation