A Review of the Effective Techniques of Compression in Medical Image Processing

Suma, Suma; Sridhar, V.

doi:10.5120/17012-7291

Cited by 11 publications

(5 citation statements)

References 22 publications

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“…Accordingly, this technique is adopted in sectors wherein dependability and data security are critical. Furthermore, lossless compression techniques are desirable for legal and ethical reasons as they perfectly recreate the original picture [33], [34].…”

Section: Lossless Medical Image Compressionmentioning

confidence: 99%

A Performance Review for Hybrid Region of Interest-Based Medical Image Compression

Aziz,

Mohd Sam,

Hassan

et al. 2023

IEEE Access

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In this modern era, medical image sharing has become a routine activity within hospital information systems. Digital medical images have become valuable resources that aid health care systems' decision-making and treatment procedures. A medical image consumes a significant amount of memory, and the size of medical images continues to grow as medical imaging technology progresses. In addition, an image is shared for analysis to support knowledge sharing and disease diagnosis. Therefore, health care systems must ensure that medical images are appropriately distributed without information loss in a timely and secure manner. Image compression is the primary process performed on each medical image before it is shared to ensure that the purpose of sharing an image is accomplished. The hybrid region of interest-based medical compression algorithms reduces image size. Furthermore, these algorithms shorten the image compression process time by manipulating the advantages of lossy and lossless compression techniques. A comprehensive review of previous studies that utilized this approach was conducted. Sample studies were selected from published articles in an open database subscribed to by Universiti Teknologi Malaysia for ten years (2012 to 2023). This work aims to critically review and comprehensively analyze previous types of algorithms by focusing on their main performance results: compression ratio, mean square error and peak signal-to-noise ratio. This article will identify which type of algorithm can give optimal value to the primary performance metric for compressing medical images. INDEX TERMS DICOM, Hybrid Image Compression, Medical Image, Performance Review, Region of Interest-based, I. INTRODUCTIONMedical images are critical resources that assist physicians and facilitate monitoring of a patient's health condition, providing a wide range of information that doctors and specialists need to make accurate diagnoses. Moreover, medical images are among the most critical assets of a health care information system (HIS). Health care practitioners utilize these images to electronically detect, diagnose, treat and evaluate various diseases.In the current technological era, image files occupy a significant amount of memory. The same issue applies to medical images. Each medical image consumes a vast amount of memory and storage space. Therefore, an HIS administrator must ensure sufficient memory space for storing medical images. In addition, the records of many patients must be retained for an extended period, consuming more space for storing images [1], [2].The primary operation that involves medical images in an HIS is medical image sharing. With the challenges that we are currently facing due to the Coronavirus disease 2019 (COVID-19) pandemic, the need for image sharing has become particularly evident as movement is restricted in many places worldwide. In many clinical cases, diagnostic planning requires collaboration between referring physicians

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Section: Lossless Medical Image Compressionmentioning

confidence: 99%

A Performance Review for Hybrid Region of Interest-Based Medical Image Compression

Aziz,

Mohd Sam,

Hassan

et al. 2023

IEEE Access

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“…CUDA [17], Qt-Threaded [18], OpenCL [19] etc are in use in the processing such massive size of the medical images. To some extent, the discussion of the problem associated with the medical image compression was done in our prior work [20]. The problems that have been identified for the proposed system are as follows:  Less Focus on Computational Capability: The existing technique mainly focuses on performing compression by reducing the sizes of medical images of uniform dimensions.…”

Section: The Problemmentioning

confidence: 99%

Design of Multiplier for Medical Image Compression Using Urdhava Tiryakbhyam Sutra

Suma

Sridhar

2016

IJECE

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Compressing the medical images is one of the challenging areas in healthcare industry which calls for an effective design of the compression algorithms. The conventional compression algorithms used on medical images doesn’t offer enhanced computational capabilities with respect to faster processing speed and is more dependent on hardware resources. The present paper has identified the potential usage of Vedic mathematics in the form of Urdhava Tiryakbhyam sutra, which can be used for designing an efficient multiplier that can be used for enhancing the capabilities of the existing processor to generate enhance compression experience. The design of the proposed system is discussed with respect to 5 significant algorithms and the outcome of the proposed study was testified with heterogeneous samples of medical image to find that proposed system offers approximately 57% of the reduction in size without any significant loss of data.

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“…As mentioned, compression and coding techniques are used in many systems, i.e. where image processing is performed, so there are techniques for medical images [13][14][15][16][17], radar images [18][19][20], satellite images [21][22][23] and for many other smart systems combining different compression and coding techniques [24][25][26]. Image processing is an integral part of machine learning and artificial intelligence, where there are classifiers and neural network models that can be used as in [27], [28].…”

Section: Introductionmentioning

confidence: 99%

Analysis of Edge Detection on Compressed Images with Different Complexities

Maksimovic¹,

Jakšić²,

Petrović³

et al. 2020

ACTA POLYTECH HUNG

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This paper provides edge detection analysis on images, which consist of different numbers of details (small, medium and high number of details) and which are compressed by different compression algorithms -JPEG, JPEG2000 and SPIHT. Images from the BSD (Berkeley Segmentation Database) database were used and compressed with different number of bits per pixel. The analysis was performed for five edge detectors: Canny, LoG, Sobel, Prewitt, and Roberts. The fidelity of the detected edges was determined using the objective measures Figure of Merit (FOM), F measure and Performance Ratio (PR), where the reference value was taken from the GroundTruth image. Based on the results presented in the tables, it can be concluded that edge detection behaves differently depending on the number of bits per pixel and applied compression algorithm, as well as, the number of details in the image. Roberts operator has been proven to be the best solution, when it is necessary to perform better edge detection over compressed images with small a number of details, but Canny shows better results for images with a high number of details.

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A Review of the Effective Techniques of Compression in Medical Image Processing

Cited by 11 publications

References 22 publications

A Performance Review for Hybrid Region of Interest-Based Medical Image Compression

A Performance Review for Hybrid Region of Interest-Based Medical Image Compression

Design of Multiplier for Medical Image Compression Using Urdhava Tiryakbhyam Sutra

Analysis of Edge Detection on Compressed Images with Different Complexities

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