Paper Info
Title
Region Of Interest-Based Coding Technique Of Medical Images Using Varying Grading Of Compression
Abstract
A region of interest (ROI)-based compression method for medical image datasets is a requirement to maintain the quality of the diagnostically important region of the image. It is always a better option to compress the diagnostic important region in a lossless manner and the remaining portion of the image with a near-lossless compression method to achieve high compression efficiency without any compromise of quality. The predictive ROI-based compression on volumetric CT medical image is proposed in this paper; resolution-independent gradient edge detection (RIGED) and block adaptive arithmetic encoding (BAAE) are employed to ROI part for prediction and encoding that reduce the interpixel and coding redundancy. For the non-ROI portion, RIGED with an optimal threshold value, quantizer with optimal q-level and BAAE with optimal block size are utilized for compression. The volumetric 8-bit and 16-bit standard CT image dataset is utilized for the evaluation of the proposed technique, and results are validated on real-time CT images collected from the hospital. Performance of the proposed technique in terms of BPP outperforms existing techniques such as JPEG 2000, M-CALIC, JPEG-LS, CALIC and JP3D by 20.31%, 19.87%, 17.77%, 15.58% and 13.66%, respectively.
Year
DOI
Venue
2021
10.1142/S0219467821500212
INTERNATIONAL JOURNAL OF IMAGE AND GRAPHICS
Keywords
DocType
Volume
Near-lossless compression, quantizer, gradient edge detector, arithmetic encoding, entropy coding, region of interest
Journal
21
Issue
ISSN
Citations 
02
0219-4678
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Urvashi Sharma100.34
Meenakshi Sood254.14
Emjee Puthooran301.69