A Novel Image Processing Approach to Enhancement and Compression of X-ray Images

Pourasad, Yaghoub; Cavallaro, Fausto

doi:10.3390/ijerph18136724

Cited by 17 publications

(5 citation statements)

References 52 publications

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“…The effectiveness of the presented compression technique is assessed based on PSNR [23], CR, mean structural similarity index (SSIM) [24] and SSS [25]. In summary, higher PSNR and SSIM indicate that the recovered images, R are identical to the original images, O.…”

Section: Performance Metricsmentioning

confidence: 99%

A novel compression methodology for medical images using deep learning for high-speed transmission

Navaneethakrishnan,

Shanmugam

2024

IJRES

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Medical imaging is a rapidly growing field having a high impact on the early detection, diagnosis and surgical planning of diseases. Several imaging techniques such as computed tomography (CT), magnetic resonance imaging (MRI) and ultrasound (US) imaging generate a higher volume of data, necessitating additional storage and communication requirements. Hence, image compression is utilized in medical field to reduce redundancy and alleviate memory and bandwidth issues. This paper presents a novel deep learning-based compression method to reduce the size of medical images. This method employs a deep convolutional neural network for learning compact representations of medical images, then coded by a Huffman encoder. The compression process is reversed to reconstruct the original image. Several tests are conducted to compare the results with other wellknown compression methods. The proposed model achieved a mean peak signal-to-noise ratio (PSNR) of 42.82 dB with storage space saving (SSS) of 96.15% for CT, 43.88 dB with SSS of 96.25% for MRI, 46.29 dB with SSS of 96.07% for US and 43.51 dB with SSS of 96.95% for X-ray images. The findings showed that the proposed compression technique could greatly compress the image size, saving storage space, facilitating better transmission and preserving critical diagnostic information.

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Section: Performance Metricsmentioning

confidence: 99%

A novel compression methodology for medical images using deep learning for high-speed transmission

Navaneethakrishnan,

Shanmugam

2024

IJRES

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“…It could be a pattern that appears repeatedly throughout the images, or it could be a pixel that appears repeatedly throughout the entire image. One or more of the three fundamental data redundancies, which are discussed below (2,3) can be removed to achieve compression.…”

Section: Image Compressionmentioning

confidence: 99%

A Novel Image Compression Algorithm Based on Autoencoder for Biometric Template Protection Scheme

Vasuki¹,

Prasanna²

2023

IJST

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Objectives: To evaluate the proposed deep learning-based method for biometric template protection schemes with other existing image compression methods. Methods: In this study, four image compression methods such as JPEG_LS, Vector quantization (VQ), Run-length encoding (RLE), and the Autoencoder methods are implemented. The experimental results are compared with the different performance parameters that are applied over the CASIA I iris dataset possessing 756 images. Findings: The proposed autoencoder method algorithm offered enhanced results in terms of PSNR, SSIM, MSE, and CR when compared to other transform methods. The deep neural network-based autoencoder algorithm achieved the highest compression ratio of 89.05 percent, while the conventional algorithms achieved the highest image quality rate of 94.05 percent. Novelty: A novel autoencoder-based image compression model has been proposed in this study. The proposed Autoencoder (AE) method incorporates five stages like Initialization, Learning compression representations, Generation of chaotic sequence, Image encryption and decryption, and Image reconstruction. The generation of the chaotic sequence using the stochastic logistic map, as well as the learning compression representations contributes towards the novelty of this study.

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“…Both lossless and lossy compression methods were employed to achieve high image quality savings when compressing medical photographs for the paper [17]. The quality of a compressed image was also improved by the application of enhancing techniques.…”

Section: Literature Reviewmentioning

confidence: 99%

Medical image compression and restoration using a hybrid neural network combining Huffman and meta-heuristic techniques

Khalifeh,

Taghizadeh,

Ghanbarian

et al. 2023

Preprint

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In order to compress medical pictures for long-term storage, two methods are used in this work. The first step is to use a neural network–based categorization system to simplify images using a hierarchical modeling technique. The Huffman cipher is then used to compress the reduced images. In the second method, a deep neural network is trained to make predictions. This method can potentially reduce the amount of data needed to describe a picture by using a trained neural network to make intelligent guesses about the location of individual pixels. Huffman compression is used to encrypt the remaining data. By using an improved spatial filtering method to the picture data, we can decode it and then use meta-heuristic algorithms like gray wolf optimization (GWO) and wild horse optimization (WHO) to rebuild the image. Without sacrificing data compression efficacy, this paves the way for a more practical implementation of the proposed techniques in cases when outcomes are uncertain. Images can be simplified using the suggested approaches, leading to faster decoding. Afterwards, performance metrics were taken and evaluated following predetermined daily procedures. The suggested approaches outperformed state-of-the-art deep learning-based systems in compressing medical images while maintaining an exceptionally high quality level.

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A Novel Image Processing Approach to Enhancement and Compression of X-ray Images

Cited by 17 publications

References 52 publications

A novel compression methodology for medical images using deep learning for high-speed transmission

A novel compression methodology for medical images using deep learning for high-speed transmission

A Novel Image Compression Algorithm Based on Autoencoder for Biometric Template Protection Scheme

Medical image compression and restoration using a hybrid neural network combining Huffman and meta-heuristic techniques

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