Lossless Compression of CT Images by an Improved Prediction Scheme Using Least Square Algorithm
| Autor: | A. Lenin Fred, H. Ajay Kumar, P. Sebastin Varghese, Subbiahpillai Neelakantapillai Kumar |
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| Rok vydání: | 2019 |
| Předmět: |
Lossless compression
0209 industrial biotechnology Computer science Applied Mathematics Vector quantization Data_CODINGANDINFORMATIONTHEORY 02 engineering and technology computer.file_format Huffman coding JPEG DICOM symbols.namesake 020901 industrial engineering & automation Compression (functional analysis) Signal Processing Median filter symbols Algorithm computer Data compression |
| Zdroj: | Circuits, Systems, and Signal Processing. 39:522-542 |
| ISSN: | 1531-5878 0278-081X |
| DOI: | 10.1007/s00034-019-01152-8 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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