# MIDOM--A DICOM-Based Medical Image Communication System
## 2 Background
### Related Work  
Many modern medical imaging devices, such as CT scanners, MRIs, or X-rays, use DICOM for information sharing and standardized output. As [PERSON] et al. [10] describe, the size of a typical CT exam had about 12 MB in the early 1990s. In 2017, a similar exam on a modern CT scanner increased the output size of the file between 600 MB and 1 GB. The size of a standard DICOM file may require higher bandwidth and storage, which is an issue even during the data migration [11]. Compression techniques for images stored in DICOM files are one approach to lowering the file size and bandwidth required. There are two main types of image compression, lossless, which retains complete image information, and lossy, which removes some information from the image. As lossy compression removes some data from the picture, it is questionable if that image is still valid for further analysis. Authors, such as [PERSON] et al. in their paper [12], analyze the impact of lossy compression on hyperspectral image classification. Authors, such as [PERSON] et al. in their work [13], describe an optimized JPEG-XT method (OPT_JPEG_XT) that better compresses 16-bit medical images than JPEG-XT and JPEG 2000. They continue their efforts to increase the compression ratio and publish their upgraded work [14] in 2023. Other authors, such as [PERSON] et al. [15], describe a lossy algorithm inspired by translating DNA sequences into protein sequences to store grayscale images, which they represent as promising compared to JPEG lossy image compression. The output file size is not the only limitation--another is computing power and running memory. [PERSON] et al. describe their approach [16] to lossy JPEG image compression. They use a new image compression algorithm based on a non-uniform partition and U-system designed for such devices. In their work [17], [PERSON] et al. propose lossless compression of CT images by an improved prediction scheme using the least square algorithm. Their implementation on Raspberry Pi shows a significant improvement compared to other algorithms, such as JPEG, JPEG lossless, BCOT, SPIHT, and Maxshift. In another work, [PERSON] et al. [18] propose Gaussian Hemite polynomial-based lossless medical image compression, which produces better results when compared with the Legendre polynomial-based compression and JPEG lossless compression. Authors, such as [PERSON] et al. [19], suggest two new chain code approaches as solutions to the large data size required to store in PACS over time. Some authors, such as [PERSON] et al. [20], even suggest combining data compression with encryption for biomedical images to achieve better data security in telemedicine. They achieve lossless compression using the hybrid of Huffman coding, linear predictive coding, and discrete wavelet. Some, such as [PERSON] et al. in their work [21], suggest implementing a blockchain token mechanism stored in DICOM files to track access to the shared medical data. PACS are generally used to store and access DICOM files. These systems are usually accessible only from inside medical institutions. There were several attempts to create mobile or web applications to facilitate peer-review data entry. In their work, [PERSON] et al. [22] analyze their responsive web application to support the logging of cases and conclude that it took much fewer clicks and screen taps than previous formalized data entry using an electronic spreadsheet. [PERSON] et al., in their work [23], researched the use of WhatsApp in health in 2017. They discovered that all health professionals in University Hospital Limerick (UHL), Ireland, who responded to their questionnaire (41/51) used WhatsApp. All of them participated in the group chat for clinical medicine at UHL. In 2017, [PERSON] et al. [6] examined the future of telehealth implementation in two cases. First is the usage of smartphones in rural areas of South Africa, where doctors take photographs of skin lesions and send them to dermatologists in regional hospitals using WhatsApp.