A combined scheme of pixel and block level splitting for medical image compression and reconstruction

Sunil, H.; Hiremath, Sharanabasaweshwar G

doi:10.1016/j.aej.2017.03.001

Cited by 20 publications

(10 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In 2018, Sunil et al [1] have suggested a model to attain the reconstruction of medical image by establishing convexsmoothing issues. This was executed by isolating the input image into sub-issues.…”

Section: A Related Workmentioning

confidence: 99%

“…Table 1 show the methods, features, and challenges of conventional techniques for the medical image compression. At first, Langragian framework was introduced in [1], which recognizes zero-day attack, and it also offers increased PSNR. However, it needs more time.…”

Section: A Related Workmentioning

confidence: 99%

“…Image compression [1] [2] [3] is the process of reducing the size of bytes in a graphics file devoid of deteriorating the image quality to an undesirable level. The minimization in file size permits more images to be stored in a specified memory space or disk.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Medical Image Compression by Optimal Filter Coefficients Aided Transforms using Modified Rider Optimization Algorithm

Sreenivasulu¹,

Rajan²,

Thenappan³

2019

IJEAT

View full text Add to dashboard Cite

Owing to a large amount of images, image compression is requisite for minimizing the redundancies in image, and it offers efficient transmission and archiving of images. This paper presents a novel medical image compression model using intelligent techniques. The adopted medical image compression comprises of three major steps such as, Segmentation, Image compression, and Image decompression. Initially, the Region of Interest (ROI) and Non-ROI regions of the image are split by means of a Segmentation procedure using Modified Region Growing (MRG) algorithm. Moreover, the image compression process begins which is varied for both ROI and Non-ROI regions. On considering the ROI regions, the compression is carried out by Discrete Cosine Transform (DCT) model and SPIHT encoding method, whereas the compression of Non-ROI region is carried out by Discrete Wavelet Transform (DWT) and Merge-based Huffman encoding (MHE) methods. As a main contribution, this paper intends to deploy the optimized filter coefficients in both DCT and DWT techniques. Here, the optimization of both filter coefficients is performed using Modified Rider Optimization Algorithm (ROA) called Improvised Steering angle and Gear-based ROA (ISG-ROA). In the final step, decompression is done by implementing the reverse concept of compression process with similar optimized coefficients. The filter coefficients are tuned in such a way that the Compression Ratio (CR) should be minimum. In addition, the comparative analysis over the state-of-the-art models proves the superior performance of the proposed model.

show abstract

Section: A Related Workmentioning

confidence: 99%

Section: A Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Medical Image Compression by Optimal Filter Coefficients Aided Transforms using Modified Rider Optimization Algorithm

Sreenivasulu¹,

Rajan²,

Thenappan³

2019

IJEAT

View full text Add to dashboard Cite

show abstract

“…The conventional techniques take higher processing time as compared to CS theory based techniques. The proposed approach in [4] is divided into three stages wherein at the foremost stage convergence theory is applied to solve the problem and after that iterative process is used to lower down the computation complexity by using the convergence and finally hybrid approach is applied for reconstruction using compression sensing. Performance is measured in terms of peak signal to noise ratio, reconstruction error and time taken to perform reconstruction.…”

Section: Literature Reviewmentioning

confidence: 99%

Performance analysis of compression algorithms for information security: A Review

Sharma

Batra

2018

ICST Transactions on Scalable Information Systems

View full text Add to dashboard Cite

Data compression is a vital part of information security, since compressed data is much more secure and convenient to handle. Effective data compression technique creates an effective, secure, easy communicable & redundant data. There are two types of compression algorithmic techniques:-lossy and lossless. These techniques can be applied to any data format like text, audio, video or image file. The primary objective of this study was to analyse data compression techniques used for information security techniques like steganography, cryptography etc. Four each, lossy and lossless techniques are implemented and evaluated on parameters like-file size, saving percentage, time, compression ratio and speed. Detailed analysis shows that the lossy techniques performs better quantitatively whereas lossless is better qualitatively. However, lossless techniques are more effective as there is no data loss during the process. Among all, Huffman encoding outperforms other algorithms.

show abstract

“…Fractal coding-based image compression can be lossless or lossy compression; it is applied for the removal of redundancy from the original data after compression. The lossless compression scheme is a promising technique to save huge medical data for medical imaging systems [15]. To signify the image data in a compressed manner, an image compression system contains an encoder that uses the redundancies while the decoder is applied to rebuild the original image from the compressed data [12].…”

Section: Introductionmentioning

confidence: 99%

An Efficient Lossless ROI Image Compression Using Wavelet-Based Modified Region Growing Algorithm

Sreenivasulu

Varadarajan

2018

Journal of Intelligent Systems

View full text Add to dashboard Cite

Abstract Nowadays, medical imaging and telemedicine are increasingly being utilized on a huge scale. The expanding interest in storing and sending medical images brings a lack of adequate memory spaces and transmission bandwidth. To resolve these issues, compression was introduced. The main aim of lossless image compression is to improve accuracy, reduce the bit rate, and improve the compression efficiency for the storage and transmission of medical images while maintaining an acceptable image quality for diagnosis purposes. In this paper, we propose lossless medical image compression using wavelet transform and encoding method. Basically, the proposed image compression system comprises three modules: (i) segmentation, (ii) image compression, and (iii) image decompression. First, the input medical image is segmented into region of interest (ROI) and non-ROI using a modified region growing algorithm. Subsequently, the ROI is compressed by discrete cosine transform and set partitioning in hierarchical tree encoding method, and the non-ROI is compressed by discrete wavelet transform and merging-based Huffman encoding method. Finally, the compressed image combination of the compressed ROI and non-ROI is obtained. Then, in the decompression stage, the original medical image is extracted using the reverse procedure. The experimentation was carried out using different medical images, and the proposed method obtained better results compared to different other methods.

show abstract

A combined scheme of pixel and block level splitting for medical image compression and reconstruction

Cited by 20 publications

References 15 publications

Medical Image Compression by Optimal Filter Coefficients Aided Transforms using Modified Rider Optimization Algorithm

Medical Image Compression by Optimal Filter Coefficients Aided Transforms using Modified Rider Optimization Algorithm

Performance analysis of compression algorithms for information security: A Review

An Efficient Lossless ROI Image Compression Using Wavelet-Based Modified Region Growing Algorithm

Contact Info

Product

Resources

About