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Paper Title : Different Wavelets For Medical Image Compression In Telemedicine’s

ISSN : 2395-1303
Year of Publication : 2021
10.29126/23951303/IJET-V7I5P8

Authors: Ms. Shubhangi Pimpalzare, Dr. S S Mungona

         


Abstract
- Transferring medical images from one center to another is common use in telemedicine. These high-quality images stored in DICOM format require higher bandwidth in transmission and large storage space in PACS (Picture Archieving and Communication System) memory. Therefore reducing the image size for preserving diagnostic information has become a need. In this sense, medical image compression is technique that overcomes both transmission and storage cost by lossy and lossless compression algorithms. There are numerous compression methods developed for region-based studies generally used in radiography, computed tomography (CT) and magnetic resonance images (MRI). The use of digital medical images is increases very fast. Medical images like CT scan, ultrasound, dental X-ray etc, require large amounts of memory storage. Even to transmit an image over a wireless or LAN network could take more time. Due to this reason medical image compression is important. Related to medical images many compression methods are available. However, the lossless ( for diagnostic and legal reasons) techniques, which allows for perfect reconstruction of original image, yield compression rates of at most 2 only, while the techniques that yields higher compression rates are lossy. To meet this challenge, we have developed a hybrid compression schemes which is diagnostically lossless with good compression ratio. Due to its simplicity the hardware realization is also easy and not cost effective compare to JPEG method. Along with that, very limited researchers take a challenge to apply hardware on their implementation. Referring to the previous work reviewed, most of the compression method used lossless rather than lossy. For implementation using software, MATLAB is the famous candidates among researchers. In term of analysis, most of the previous works conducted objective test compared with subjective test. This paper thoroughly reviews the recent advances in medical image compression mainly in terms of types of compression, software and hardware implementations and performance evaluation. Furthermore, challenges and open research issues are discussed in order to provide perspectives for future potential research. In conclusion, the overall picture of the image processing landscape, where several researchers more focused on software implementation and various combination of software implementation.

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Keywords
- Matlab, MSE(mean square error);PSNR(peak signal to noise ratio);COC(correlation coefficient);Huffman coding ;CT scan images; Ultrasound images; Dental X- ray