Region-of-interest-based wavelet compression scheme for medical images

1997 ◽  
Author(s):  
Dongkyoo Shin ◽  
Hsien-Hsun S. Wu ◽  
Jyh-Charn S. Liu
Keyword(s):  
Medical Images ◽  
Region Of Interest ◽  
Compression Scheme ◽  
Wavelet Compression

scholarly journals OFCS: Optimized Framework of Compressive Sensing for Medical Images in Bottleneck Network Condition

2018 ◽  
Vol 8 (5) ◽  
pp. 2829
Author(s):  
Lakshminarayana M ◽  
Mrinal Sarvagya
Keyword(s):  
Compressive Sensing ◽  
Medical Image ◽  
Medical Images ◽  
Region Of Interest ◽  
Lossless Compression ◽  
Cost Effective ◽  
Signal Quality ◽  
Effective Solution ◽  
The Cost

Compressive sensing is one of teh cost effective solution towards performing compression of heavier form of signals. We reviewed the existing research contribution towards compressive sensing to find that existing system doesnt offer any form of optimization for which reason the signal are superiorly compressed but at the cost of enough resources. Therefore, we introduce a framework that optimizes the performance of the compressive sensing by introducing 4 sequential algorithms for performing Random Sampling, Lossless Compression for region-of-interest, Compressive Sensing using transform-based scheme, and optimization. The contribution of proposed paper is a good balance between computational efficiency and quality of reconstructed medical image when transmitted over network with low channel capacity. The study outcome shows that proposed system offers maximum signal quality and lower algorithm processing time in contrast to existing compression techniuqes on medical images.

scholarly journals An Intelligent cluster-based segmentation using Wavelet Features:Application to Medical Images

2010 ◽  
Author(s):  
Thavavel V ◽  
JafferBasha J
Keyword(s):  
Medical Images ◽  
Automatic Segmentation ◽  
A Priori ◽  
User Interaction ◽  
Region Of Interest ◽  
Feature Space ◽  
Discrete Wavelet ◽  
Wavelet Frames ◽  
Translation Invariant ◽  
Spherical Coordinate

Segmentation forms the onset for image analysis especially for medical images, making any abnormalities in tissues distinctly visible. Possible application includes the detection of tumor boundary in SPECT, MRI or electron MRI (EMRI). Nevertheless, tumors being heterogeneous pose a great problem when automatic segmentation is attempted to accurately detect the region of interest (ROI). Consequently, it is a challenging task to design an automatic segmentation algorithm without the incorporation of ‘a priori’ knowledge of an organ being imaged. To meet this challenge, here we propose an intelligence-based approach integrating evolutionary k-means algorithm within multi-resolution framework for feature segmentation with higher accuracy and lower user interaction cost. The approach provides several advantages. First, spherical coordinate transform (SCT) is applied on original RGB data for the identification of variegated coloring as well as for significant computational overhead reduction. Second the translation invariant property of the discrete wavelet frames (DWF) is exploited to define the features, color and texture using chromaticity of LL band and luminance of LH and HL band respectively. Finally, the genetic algorithm based K-means (GKA), which has the ability to learn intelligently the distribution of different tissue types without any prior knowledge, is adopted to cluster the feature space with optimized cluster centers. Experimental results of proposed algorithm using multi-modality images such as MRI, SPECT, and EMRI are presented and analyzed in terms of error measures to verify its effectiveness and feasibility for medical applications.

An efficient technique for secure transmission of the region of interest in medical images

2014 ◽  
Vol 7 (2) ◽  
pp. 116
Author(s):  
Zahia Brahimi ◽  
Mohamed Khireddine Kholladi ◽  
Hamid Bessalah ◽  
Amina Tarabet ◽  
Fadila Mostefai
Keyword(s):  
Medical Images ◽  
Region Of Interest ◽  
Efficient Technique ◽  
Secure Transmission
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