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As the increasing of digital imagilag modalities,a close-at-hand challenge to deal with is the storage and transmission requirement of enormous data of medical images.Compression is one of the indispensable techniques to solve this problem.A comprehensive review and discussions are made in this paper over the medial image compression techniques applied in medical image domain,including the latest achievements in this field.Different compression algorithm including ROI-based coding,lossless compression,DWT,neural net are introduced and some quality evaluation methods are introduced.Foreground of the field is given from our point of view.
RESUMO
Convention dictates that standards are a necessity rather than a luxury. Standards are supposed to improve the exchange of health and image data information resulting in improved quality and efficiency of patient care. True standardisation is some time away yet, as barriers exist with evolving equipment, storage formats and even the standards themselves. The explosive growth in the size and complexity of images such as those generated by multislice computed tomography have driven the need for digital image management, created problems of storage space and costs, and created a challenge for increasing or getting an adequate speed for transmitting, accessing and retrieving the image data. The search for a suitable and practical format for storing the data without loss of information and medico-legal implications has become a necessity and a matter of ‘urgency’. Existing standards are either open or proprietary and must comply with local, regional or national laws. Currently there are the Picture Archiving and Communications System (PACS); Digital Imaging and Communications in Medicine (DICOM); Health Level 7 (HL7) and Integrating the Healthcare Enterprise (IHE). Issues in digital image management can be categorised as operational, procedural, technical and administrative. Standards must stay focussed on the ultimate goal – that is, improved patient care worldwide.
RESUMO
Objective To conduct lossless compression to medical images with lossless regions of interest.Methods Firstly,the shape information of interest regions were segmented and pick up.Then,the self-adaptive wavelet transform was performed to transform interest regions.At last,the modulus of the interest regions and the rest were coded respectively before being stored or transmitted.Results A method for medical image compression based on shape-self-adapted ROI was raised.Conclusion The whole image can be highly compressed with high fidelity