Lossless Compression of CT Images by an Improved Prediction Scheme Using Least Square Algorithm
The storage and transmission of medical data such as CT/MR DICOM images are an essential part of the telemedicine application. In this paper, a prediction-based lossless compression algorithm using least square approach is proposed for the compression of CT images. Prior to compression, the preproce...
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| Veröffentlicht in: | Circuits, systems, and signal processing systems, and signal processing, 2020-02, Vol.39 (2), p.522-542 |
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| container_title | Circuits, systems, and signal processing |
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| creator | Kumar, S. N. Fred, A. Lenin Kumar, H. Ajay Varghese, P. Sebastin |
| description | The storage and transmission of medical data such as CT/MR DICOM images are an essential part of the telemedicine application. In this paper, a prediction-based lossless compression algorithm using least square approach is proposed for the compression of CT images. Prior to compression, the preprocessing was performed by neutrosophic median filter. The gradient adjusted prediction scheme was employed for the determination of prediction coefficients, and polynomial least square fitting approach was used for optimal selection of prediction coefficients. The selected prediction coefficients are finally encoded by Huffman coder for transmission. The quality of the reconstructed image was validated by performance metrics and compared with other compression techniques like JPEG, contextual vector quantization and vector quantization using bat optimization (BAT-VQ). The proposed neutrosophic set-based least square compression algorithm was found to be efficient and tested on DICOM abdomen CT datasets. The hardware implementation was done by Raspberry Pi processor using Java platform for transferring the data through cloud network for telemedicine application. |
| doi_str_mv | 10.1007/s00034-019-01152-8 |
| format | Article |
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The quality of the reconstructed image was validated by performance metrics and compared with other compression techniques like JPEG, contextual vector quantization and vector quantization using bat optimization (BAT-VQ). The proposed neutrosophic set-based least square compression algorithm was found to be efficient and tested on DICOM abdomen CT datasets. 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Ajay</creatorcontrib><creatorcontrib>Varghese, P. Sebastin</creatorcontrib><title>Lossless Compression of CT Images by an Improved Prediction Scheme Using Least Square Algorithm</title><title>Circuits, systems, and signal processing</title><addtitle>Circuits Syst Signal Process</addtitle><description>The storage and transmission of medical data such as CT/MR DICOM images are an essential part of the telemedicine application. In this paper, a prediction-based lossless compression algorithm using least square approach is proposed for the compression of CT images. Prior to compression, the preprocessing was performed by neutrosophic median filter. The gradient adjusted prediction scheme was employed for the determination of prediction coefficients, and polynomial least square fitting approach was used for optimal selection of prediction coefficients. The selected prediction coefficients are finally encoded by Huffman coder for transmission. 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| subjects | Algorithms Circuits and Systems Coding Coefficients Computed tomography Electrical Engineering Electronics and Microelectronics Engineering Image compression Image quality Image reconstruction Image transmission Instrumentation JPEG encoders-decoders Least squares Medical imaging Microprocessors Optimization Performance measurement Polynomials Signal,Image and Speech Processing Telemedicine Vector quantization |
| title | Lossless Compression of CT Images by an Improved Prediction Scheme Using Least Square Algorithm |
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