Video compression of coronary angiograms based on discrete wavelet transform with block classification

Coronary angiogram video compression adapted to medical imaging applications

H.264/AVC coder has proven to use the most advanced video compression, but, at the cost of high computational complexity. On the other hand, analysis of coronary x-ray images reveals large areas containing no diagnostically important information. In this paper, we propose to exploit the energy characteristics in slice equal size regions to determine the active zones in coronary x-ray sequences to be encoded as normal using the H.264 coding system. The other regions, are compressed using conventional low complex approaches. Experimental results have shown that this procedure reduces the coder computing time of about 20% while attaining the same compression performance. A clinical subjective assessment by three expert physicians in interventional cardiology leads to a compression ratio of about 30:1 which insures both a diagnosis adequacy and a sufficient compression in regards to storage and transmission requirements.

Automatic quality control for wavelet-based compression of volumetric medical images using distortion-constrained adaptive vector quantization

The enormous data of volumetric medical images (VMI) bring a transmission and storage problem that can be solved by using a compression technique. For the lossy compression of a very long VMI sequence, automatically maintaining the diagnosis features in reconstructed images is essential. The proposed wavelet-based adaptive vector quantizer incorporates a distortion-constrained codebook replenishment (DCCR) mechanism to meet a user-defined quality demand in peak signal-to-noise ratio. Combining a codebook updating strategy and the well-known set partitioning in hierarchical trees (SPIHT) technique, the DCCR mechanism provides an excellent coding gain. Experimental results show that the proposed approach is superior to the pure SPIHT and the JPEG2000 algorithms in terms of coding performance. We also propose an iterative fast searching algorithm to find the desired signal quality along an energy-quality curve instead of a traditional rate-distortion curve. The algorithm performs the quality control quickly, smoothly, and reliably.

A wavelet-based region of interest encoder for the compression of angiogram video sequences

A new method for the compression of angiogram video sequences is presented. The method is based on the philosophy that diagnostically significant areas of the image should be allocated the greatest proportion of the total allocated bit budget. The approach uses a three-dimensional wavelet-coder based on the popular set partitioning in hierarchical trees algorithm. Incorporated into this framework are a region-of-interest (ROI) detection stage and a texture-modeling stage. The combined result is an approach that models the high-frequency wavelet coefficients for some diagnostically unimportant regions of the image in an extremely efficient manner. This allows additional bits to be used within the ROI to improve the quality of the diagnostically significant areas. Results are compared for a number of real data sets and evaluated by trained cardiologists.

Selective medical image compression techniques for telemedical and archiving applications

Selective Image Compression (SeLIC) is a compression technique where explicitly defined regions of interest (RoI) are compressed in a lossless way whereas image regions containing unimportant information are compressed in a lossy manner. Such techniques are of great interest in telemedicine or medical imaging applications with large storage requirements. In this paper we introduce and compare techniques with different functionalities. Moreover, we investigate the impact of using wavelet transforms and JPEG as underlying lossy compression algorithm.