Abstract
The continuous utilization of massive patient data via telecommunication medium is raising a concern either in data transmission speed, storage, security and privacy. The introduction of Informatization, Internet of Things (IoT), Big Data Technology, and e-health require effective data compression techniques that will help solve the numerous challenges evident in the conventional medical image compression schemes. In order to successfully transmit medical data via the network of networks demands an efficient data compression mechanisms without reduction in the image quality with reduced size. This mechanism greatly minimizes costs, provides mobility and comfort to the users, increase speed in medical file transmission and lot of more. The research investigates the various medical image compression platforms so, as to achieve efficient and effective scheme. Medical image compression require more proactive scheme that maintains vital features of patients. Several compression methods were applied and Discrete Cosine Transform (DCT) proved to have a superior compression ratio as opposed to Discrete Wavelet Transform (DWT). The proposed study indicated that the recovered medical images had similar results compared to the original image data. Finally, the research mitigated data storage issue of hard drive, reduce transmission time, improved patient’s mobility and the high cost of medical hardware devices.
Highlights
There are several medical applications globally that utilize data compression schemes in order to process the medical image used in e-health, telemedicine, e-medicine, ediagnostic analysis, and other medical data methods [1,2]. This is attributed to the numerous advances in informatization, big data technology, cloud computing and Internet of Things (IoT) requiring large data storage [3,4,5]
Medical image compression is needed to help increase transmission rate with fewer megabytes that can be recovered at the receiving computer without lost in the image quality [12]
The research adopted a combined schemes of Huffman coding, DWT and Discrete Wavelet Transform (DCT)
Summary
There are several medical applications globally that utilize data compression schemes in order to process the medical image used in e-health, telemedicine, e-medicine, ediagnostic analysis, and other medical data methods [1,2] This is attributed to the numerous advances in informatization, big data technology, cloud computing and IoT requiring large data storage [3,4,5]. Digitizing the medical image is paramount, and paves an efficient mechanism to store and recover dataset without degradation in the image quality [7,8] Numerous researches both academic and scientific have highlighted the need for superb medical image compression techniques. This is vital, as it helps hardware designers to minimize or redesign medical device that offers mobility, cost-effectiveness and comfortability to patients. Medical image compression is needed to help increase transmission rate with fewer megabytes that can be recovered at the receiving computer without lost in the image quality [12]
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