S-Transform Analysis Technique And Its Application On Despeckling Of MCE Images

Coronary Disease has been one of the main death reasons for human being. Real Time Myocardial Contrast Echocardiography (RT-MCE) is a new hurtless technique for quantitative estimate on cardiac microcirculation, and is starting to be introduced into clinical practice. There are two key problems for MCE Coronary Disease quantitative analysis: Register of MCE heart sequence images and the despecking of MCE image. Until now, all the reported quantitative analysis software systems for MCE haven't solved them.High quality image is the precondition and assurance for MCE Coronary Disease quantitative analysis. MCE image contains serious speckle noise, and it is a kind of very complex multiplicative noise. There are many literatures on despecking of medical ultrasound image, but the one for MCE image is very limited. The methods about despecking of MCE image contain: nonlinear wavelet shrinkage techniques, nonlinear wavelet diffusion method, biased motion-adaptive temporal filtering method. All the researches above have great theory meaning and academic value, but the clinical practice effect doesn't satisfy very well. So, it need study new method to solve this problem.R. G. Stockwell, a Canadian scholar, proposed the 1-D S-Transform theory in 1996. He has proved that we can derive S-transform as the "phase correction" of the continuous wavelet transform. So, in the frame of multiple resolution, S-Transform is an extension of wavelet transform. As a new local multi-scale spectrum analysis technology, S-Transform has distinct advantages then wavelet transform and short time Fourier transform (STFT). As a result, S-Transform analysis technique must has great potential on medical image despeckling and enhancement. So, we decided to study S-Transform analysis technique and explore its application on despeckling of MCE images.In this case, we have studied the theory, technique, and the realization of S-transform in-depth. At the same time, through considering of wavelet transform, we developed noise reduction and contrast enhancement algorithm based on S-transform analysis technique. Further more, we designed a more effective speckle noise reduction and contrast enhancement algorithm for MCE image based on S-transform analysis technique. For this algorithm, we did experiments on clinical practice MCE images, and the results of MSE, SMSE estimating standards show us that our method does better than other several methods on speckle noise reduction.At the same time, the S-Transform of a 2-D image produces a four-dimensional structure, so, the overall complexity for the 2-D S-Transform is O[N~4 log(N)], and the memory requirement grows as N~4 . Thus, a 256*256 image requires 256~4 storage elements, 32 GB of floating point values for example. These memory requirements pose problems not only for ultimate long-term storage but also for the execution of an ST, like the Fourier Transform without fast algorithm. So, in the absence of fast ST algorithm, we decided using parallel computing to solve these two problems; after investigation, we chose Linux PC cluster as the parallel computing platform.