Region of Interest Based Compression of Medical Images Using Vector Quantization

Rani, M. Mary Shanthi; Chitra, P

doi:10.5121/ijcsity.2016.4103

Cited by 4 publications

(4 citation statements)

References 5 publications

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“…Studies [7]- [9], [12], [13], [15]- [17], [19]- [22], [24]- [31], and [35] used transformations (DWT, CT, IWT, and curvelet transform). These algorithms are characterised by their speed and their ability to segment the image data to the lowest level to reach the surplus and remove it without affecting the image after its retrieval.…”

Section: Discussionmentioning

confidence: 99%

“…Studies [7], [16] and [26], whose algorithms used VQ techniques, are suitable for compressing lost data and can also be used to correct missing data and estimate density. It is considered a simple method that does not need much time and the amount of error is small, but it is of the lossy compression type.…”

Section: Discussionmentioning

confidence: 99%

“…Effective lossless compression techniques for medical images are therefore required [1], [6]. There are many lossless image compression techniques, such as run-length coding, Huffman coding, LZW coding, lossless Joint Photographic Experts Group (JPEG format), and arithmetic coding [1], [7].…”

Section: Figure 1 Medical Image Typesmentioning

confidence: 99%

“…Rani and Chitra [7] proposed a method that focuses on compressing medical images with different codebook sizes using a vector quantization method for the ROI and non-ROI. There are three stages to the proposed method.…”

Section: Vector Quantization (Vq) Techniquementioning

confidence: 99%

See 3 more Smart Citations

Significant medical image compression techniques: a review

Abduljaleel

Khaleel

2021

TELKOMNIKA

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Telemedicine applications allow the patient and doctor to communicate with each other through network services. Several medical image compression techniques have been suggested by researchers in the past years. This review paper offers a comparison of the algorithms and the performance by analysing three factors that influence the choice of compression algorithm, which are image quality, compression ratio, and compression speed. The results of previous research have shown that there is a need for effective algorithms for medical imaging without data loss, which is why the lossless compression process is used to compress medical records. Lossless compression, however, has minimal compression ratio efficiency. The way to get the optimum compression ratio is by segmentation of the image into region of interest (ROI) and non-ROI zones, where the power and time needed can be minimised due to the smaller scale. Recently, several researchers have been attempting to create hybrid compression algorithms by integrating different compression techniques to increase the efficiency of compression algorithms.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Figure 1 Medical Image Typesmentioning

confidence: 99%

Section: Vector Quantization (Vq) Techniquementioning

confidence: 99%

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Significant medical image compression techniques: a review

Abduljaleel

Khaleel

2021

TELKOMNIKA

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Hiding Region of Interest in Image using Fuzzy Logic

Kharofa

Al-khayyat

2021

J. Phys.: Conf. Ser.

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In this research, a new method for hiding image has been proposed. The proposed method relied on hiding the region of interest in the image by specifying it using fuzzy logic. Images play a large and important role in several areas. Therefore, The more important the image, the greater the need to protect and preserve it. In this research, the work was divided into two parts, the first part represents the sending side and in it the face area is defined as an important region in the image and hidden then, while the second part, represents the receiving side and in it the hidden image is received and unhide. Fuzzy logic was used in the process. The proposed algorithm provide security to the images being transmitted. Matlab language was use to perform this work.

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Region of Interest-Based Coding Technique of Medical Images Using Varying Grading of Compression

Sharma

Sood

Puthooran

2020

Int. J. Image Grap.

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A region of interest (ROI)-based compression method for medical image datasets is a requirement to maintain the quality of the diagnostically important region of the image. It is always a better option to compress the diagnostic important region in a lossless manner and the remaining portion of the image with a near-lossless compression method to achieve high compression efficiency without any compromise of quality. The predictive ROI-based compression on volumetric CT medical image is proposed in this paper; resolution-independent gradient edge detection (RIGED) and block adaptive arithmetic encoding (BAAE) are employed to ROI part for prediction and encoding that reduce the interpixel and coding redundancy. For the non-ROI portion, RIGED with an optimal threshold value, quantizer with optimal [Formula: see text]-level and BAAE with optimal block size are utilized for compression. The volumetric 8-bit and 16-bit standard CT image dataset is utilized for the evaluation of the proposed technique, and results are validated on real-time CT images collected from the hospital. Performance of the proposed technique in terms of BPP outperforms existing techniques such as JPEG 2000, M-CALIC, JPEG-LS, CALIC and JP3D by 20.31%, 19.87%, 17.77%, 15.58% and 13.66%, respectively.

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Region of Interest Based Compression of Medical Images Using Vector Quantization

Abstract: In this paper, a Region of Interest(ROI) based compression method is proposed using

Cited by 4 publications

References 5 publications

Significant medical image compression techniques: a review

Significant medical image compression techniques: a review

Hiding Region of Interest in Image using Fuzzy Logic

Region of Interest-Based Coding Technique of Medical Images Using Varying Grading of Compression

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