Coefficient Random Permutation Based Compressed Sensing for Medical Image Compression

Monika, R.; Dhanalakshmi, Samiappan; Sreejith, S.

doi:10.1007/978-981-10-4765-7_56

Cited by 14 publications

(3 citation statements)

References 7 publications

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“…Therefore, the research will overcome these shortcomings and increase the reconstructed quality of the compressed picture with a high compression rate for a medical image. The author introduced a new approach to image modification for visually acceptable images in [ 23 ].…”

Section: Literature Reviewmentioning

confidence: 99%

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

Pourasad

Cavallaro

2021

IJERPH

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At present, there is an increase in the capacity of data generated and stored in the medical area. Thus, for the efficient handling of these extensive data, the compression methods need to be re-explored by considering the algorithm’s complexity. To reduce the redundancy of the contents of the image, thus increasing the ability to store or transfer information in optimal form, an image processing approach needs to be considered. So, in this study, two compression techniques, namely lossless compression and lossy compression, were applied for image compression, which preserves the image quality. Moreover, some enhancing techniques to increase the quality of a compressed image were employed. These methods were investigated, and several comparison results are demonstrated. Finally, the performance metrics were extracted and analyzed based on state-of-the-art methods. PSNR, MSE, and SSIM are three performance metrics that were used for the sample medical images. Detailed analysis of the measurement metrics demonstrates better efficiency than the other image processing techniques. This study helps to better understand these strategies and assists researchers in selecting a more appropriate technique for a given use case.

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Section: Literature Reviewmentioning

confidence: 99%

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

Pourasad

Cavallaro

2021

IJERPH

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“…This process of removing redundant information is known as image compression. Various techniques have been suggested by multiple researchers in medical sciences, computer networking and communication, image processing, and many other fields to achieve a high image compression, such as Monika et al, 3 who presented the concept of compressed sensing method for medical image compression. This concept uses coefficient random permutation-based compression system.…”

Section: Introductionmentioning

confidence: 99%

An efficient JPEG image compression based on Haar wavelet transform, discrete cosine transform, and run length encoding techniques for advanced manufacturing processes

Khan

Nazir

Hussain

et al. 2019

Measurement and Control

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Image compression plays a key role in the transmission of an image and storage capacity. Image compression aims to reduce the size of the image with no loss of significant information and no loss of quality in the image. To reduce the storage capacity of the image, the image compression is proposed in order to offer a compact illustration of the information included in the image. Image compression exists in the form of lossy or lossless. Even though image compression mechanism has a prominent role for compressing images, certain conflicts still exist in the available techniques. This paper presents an approach of Haar wavelet transform, discrete cosine transforms, and run length encoding techniques for advanced manufacturing processes with high image compression rates. These techniques work by converting an image (signal) into half of its length which is known as “detail levels”; then, the compression process is done. For simulation purposes of the proposed research, the images are segmented into 8 × 8 blocks and then inversed (decoded) operation is performed on the processed 8 × 8 block to reconstruct the original image. The same experiments were done on two other algorithms, that is, discrete cosine transform and run length encoding schemes. The proposed system is tested by comparing the results of all the three algorithms based on different images. The comparison among these techniques is drawn on the basis of peak signal to noise ratio and compression ratio. The results obtained from the experiments show that the Haar wavelet transform outperforms very well with an accuracy of 97.8% and speeds up the compression and decompression process of the image with no loss of information and quality of image. The proposed study can easily be implemented in industries for the compression of images. These compressed images are suggested for multiple purposes like image compression for metrology as measurement materials in advanced manufacturing processes, low storage and bandwidth requirements, and compressing multimedia data like audio and video formats.

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“…Compressive sensing is a promising sparse learning method that has been applied to medical diagnosis in recent years 5–7 . In the context of gene expression data classification, compressive sensing aims to learn the sparse representation of out‐of‐sample data as a linear combination of a small number of training data.…”

Section: Introductionmentioning

confidence: 99%

Bidirectional compressive sensing for classification of gene expression data

Fan

et al. 2018

Concurrency and Computation

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Summary The classification of gene expression data is significantly important for medical diagnosis. In recent years, compressive sensing emerges as a popular sparse learning method and has been applied in different areas. It is featured with the sparse representation of data with a few atoms in the dictionary. However, the traditional compressive sensing model only focuses on the relationship among different samples but neglects the relationship among different genes. In order to take into account of the both kinds of correlation, we propose a novel bidirectional compressive sensing model for the classification of gene expression data. Under this model, we develop a novel Bi‐ADMM algorithm with three different variants to solve the optimization problem. The promising experimental results on the real‐world gene expression datasets demonstrate both the effectiveness and efficiency of our proposed approach.

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Coefficient Random Permutation Based Compressed Sensing for Medical Image Compression

Cited by 14 publications

References 7 publications

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

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

An efficient JPEG image compression based on Haar wavelet transform, discrete cosine transform, and run length encoding techniques for advanced manufacturing processes

Bidirectional compressive sensing for classification of gene expression data

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