Compression of pre-scan-converted echocardiographic video using wavelet packet and integer wavelet transforms

Hang, Xiyi; Greenberg, Neil L.; Thomas, James D.

doi:10.1016/j.imavis.2006.02.022

Cited by 3 publications

(1 citation statement)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…50 images are used to study this parameter. Table 3 shows a comparison of the encoding/decoding speeds for different algorithms [39]. To study the impact of the wavelet filter on the performance of the algorithm, 100 images are used to test the performance of the algorithm at a compression rate of 40.…”

Section: Resultsmentioning

confidence: 99%

A Combined Fractal and Wavelet Angiography Image Compression Approach

Al–Fahoum¹,

Harb²

2013

TOMIJ

View full text Add to dashboard Cite

In this paper, a combined Fractal and Wavelet (CFW) compression algorithm targeting x-ray angiogram images is proposed. Initially, the image is decomposed using wavelet transform. The smoothness of the low frequency part of the image appears as an approximation image with higher self similarities, therefore, it is coded using a fractal coding technique. However, the rest of the image is coded using an adaptive wavelet thresholding technique. This model is implemented and its performance is compared with best performances of the available published algorithms. A data set containing 1000 x-ray angiograms is used to study the performance of the algorithm. A minimum compression ratio of 30 with a peak signal to noise ratio (PSNR) of 36 dB and percent diameter stenosis deviation of (<0.2%) was achieved. Results demonstrate the effectiveness of the proposed technique in obtaining a diagnostic quality of reconstructed images at very low bit rates.

show abstract

Section: Resultsmentioning

confidence: 99%

A Combined Fractal and Wavelet Angiography Image Compression Approach

Al–Fahoum¹,

Harb²

2013

TOMIJ

View full text Add to dashboard Cite

show abstract

A Comprehensive Compression and Encryption Scheme for Secured Medical Images Communication

Kumar¹,

Parthasarathy²,

Kumar³

et al. 2016

Indian Journal of Science and Technology

View full text Add to dashboard Cite

High quality model for compression of medical images in telemedicine

Moorthi¹,

Amutha²

2013

Sci. Res. Essays

View full text Add to dashboard Cite

Generally lossless compression should be used for ROI (Region of Interest) and lossy compression should be used for ROB (Region of Background) with a lower quality. In existing system, Region of Interest (ROI) is selected manually, but ROI is selected automatically in the proposed method, preprocessing is done to improve the visual quality of the image. Segmentation is carried out accurately and efficiently using region growing followed by morphological processing method. The features are extracted and classification is done in medical image using Fuzzy logic. ROB part of an image is compressed using SPIHT (Set Partition In Hierarchical Tree) algorithm in near lossless manner. Finally the ROI is superimposed in compressed Non ROI (ROB) image. This method improves the compression ratio and increase the PSNR value compared to existing method. The proposed method is used for implementations of teleradiology and digital picture archiving and communications (PACS) systems practically.

show abstract

Compression of pre-scan-converted echocardiographic video using wavelet packet and integer wavelet transforms

Cited by 3 publications

References 23 publications

A Combined Fractal and Wavelet Angiography Image Compression Approach

A Combined Fractal and Wavelet Angiography Image Compression Approach

A Comprehensive Compression and Encryption Scheme for Secured Medical Images Communication

High quality model for compression of medical images in telemedicine

Contact Info

Product

Resources

About