Abstract
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.
Highlights
Medical imaging has a great impact on the diagnosis, recognition, and surgical planning of diseases
The region of interest (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
The proposed approach of image compression for medical image datasets and the results are evaluated with the compression ratio, peak signal-to-noise ratio (PSNR), average difference, cross-correlation, and normalized absolute error (NAE)
Summary
Medical imaging has a great impact on the diagnosis, recognition, and surgical planning of diseases. Medical images contain a huge amount of data that help doctors in analyzing the condition efficiently and planning the diagnosis for patients The storage of these medical images is a crucial task for hospitals because of storage requirements. Images lose some information, which causes risk during treatment or diagnosis; designing an efficient algorithm for compression and reconstruction is important to preserve image quality and reduce the computational time for transmission. Varadarajan: Lossless ROI Image Compression Using MRG accepted, and lossless techniques are used for applications that cannot afford any loss of information (e.g. in the medical field) [22]. 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. MRG provides an accurate ROI in standard low-resolution images with complete and fulfilled segmentation structure
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