Object Separation from Medical X-Ray Images Based on ICA

X-ray medical image can examine diseased tissue of patients and has important reference value for medical diagnosis. With the problems that traditional X-rayimages have noisepoor level sense and blocked aliasing organs, this paper proposes a method for the introduction of multi-spectrum X-ray imaging and independent component analysis (ICA) algorithm to separate the target object. Firstly image de-noising preprocessing ensures the accuracy of target extraction based on independent component analysis and sparse code shrinkage. Then according to themain proportion of organ in the images, aliasing thickness matrix of each pixel was isolated. Finally independent component analysis obtains convergence matrix to reconstruct the target object with blind separation theory. In the ICA algorithm, it found that when the number is more than 40, the target objects separate successfully with the aid of subjective evaluation standard. And when theamplitudes of the scale are in the [25, 45] interval, the target images havehigh contrast and less distortion. The three-dimensional figure of Peak signal to noise ratio (PSNR) shows that the. different convergence times and amplitudes have a greater influence on image quality. The contrast and edge informationof experimental images achieve better effects with the convergence times 85 andamplitudes 35 in the ICA algorithm.