Performance Analysis of Region of Interest Based Compression Method for Medical Images

Shah, Rushabh; Sharma, Priyanka; Shah, Rutvi

doi:10.1109/acct.2014.64

Cited by 5 publications

(1 citation statement)

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“…The image is then l with ROI part set to zero. These image separately and finally after decompression [11]. calculated through n evaluated for its ethodology OI and extract the The first method is tioning the medical t this process is a lex techniques for so the segmentation The process of of any affected when dealing with approach is the s for top, right and and modeled as a n expressed by the ocated.…”

Section: Locating Roimentioning

confidence: 99%

Implementation of region based medical image compression for telemedicine application

Sophia

Anitha

2014

2014 IEEE International Conference on Computational Intelligence and Computing Research

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There is an increasing demand for transmitting medical images for telemedicine application. These medical images are rich in radiological information and the file sizes associated with it are also large. Medical images such as MRI (Magnetic Resonance Imaging) produce human body pictures in digital form and they produce excessive amount of data. So compression is necessary for storage and transmission purpose. Image compression enhances the performance of any digital system by reducing the time and cost with or without reduction in image quality. The algorithms used in this paper utilize Region Of Interest (ROI) based compression which again increases the compression ratio compared to the traditional block based methods. The results of 3 different classical algorithms with and without ROI are compared and the results are reported. ROI is coded with good fidelity than NROI and also incorporates progressive transmission. Distortion rates and compression rates are evaluated for all images and the results are compared.

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Section: Locating Roimentioning

confidence: 99%