Abstract
The increase of information in the medical environment caused by digital imaging settings is notable. The search and use of these technological tools aimed at medicine require a greater availability of storage, generating increasing costs. In medicine, together with information technology, there is a format of images used in exams, diagnostics, tomography, among others. This format, entitled DICOM, was created in order to standardize uses in medical devices for exam answers. An open question is the compression of DICOM data, in order to maintain quality, maintaining high rates of compression. This presents a new method for compressing and decompressing DICOM data using a dual cone bijector function and a video codec, called DC (Double Cone). This work offers 3 changes to the DC method (DC1, DC2 and DC3). The results obtained with a new technique show that the compression, although with loss, has a similarity index very close to the original image (SSIM = 0.99), and an accuracy ratio equal to 69.51, in the better case. The better performing version was the DC2.
Highlights
The 1980s were marked by an expansion of digital medical imaging techniques, and the consequent need to standardize access to them
The growing volume of data generated by medical imaging modalities, such as computed tomography (CT) scanners, Magnetic Resonance Imaging (MRI), Computed Radiography (CR), Digital Radiography (DR), Ultrasound (US), Nuclear Medicine (NM), Mammography and Digital Angiography have fostered discussions, especially regarding the need for storage (Huang, 2019; Rahmat et al, 2019)
We present the proposed method for compression and decompression of Digital Imaging and Communications in Medicine (DICOM) images entitled as Double Cone (DC)
Summary
The 1980s were marked by an expansion of digital medical imaging techniques, and the consequent need to standardize access to them. The growing volume of data generated by medical imaging modalities, such as computed tomography (CT) scanners, Magnetic Resonance Imaging (MRI), Computed Radiography (CR), Digital Radiography (DR), Ultrasound (US), Nuclear Medicine (NM), Mammography and Digital Angiography have fostered discussions, especially regarding the need for storage (Huang, 2019; Rahmat et al, 2019). The performance of the implemented method is evaluated using some essential criteria: the compression rate obtained, the compression gain and the quality of the reconstructed image using PSNR, MSE and SNR. The generated result showed that Wavelets Biorthogonal offers better compression size, compression rate and compression gain, but image quality parameters such as PSNR and MSE are degraded. The work defends the hypothesis that a compression method based on a dual cone bijector function and a video codec can produce compression and decompression in DICOM data with better compression rates, signal noise and similarity
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