Application of Genetic Algorithm in Image Segmentation of Gene Chips / 医疗卫生装备

Objective The image segmentation of low-density gene chips based on genetic algorithm, which can implement the function of region identification, is achieved. Methods After image denoising by wavelet analysis, image segmentation is accomplished by genetic algorithm. Results The method can detect the region of sampling point more precisely. It can also effectively separate the valuable weak signal points and background or noise. Conclusion The method can accomplish the function of image segmentation of low-density gene chips, which can provide relative accurate data information for future image analysis

Progress of medical imaging technology / 医疗卫生装备

In recent years,the medical imaging technology has developed rapidly.New imaging methods are unceasingly emerging,and the existed imaging modes have been continuously improved.The trends of medical imaging technology have developed from two-dimension imaging to three-dimension imaging,from localized imaging to whole-body imaging,from static imaging to dynamic imaging and from structure to function.This thesis introduces the new imaging methods,such as Molecular Imaging,Near Infrared Imaging and so on.

Image Pre-processing of Gene Chips Based on MATLAB / 医疗卫生装备

Objective To design an image pre-processing system of low-density gene chips based on MATLAB, which can process the colored images by cy3 and cy5 fluorescence staining of low-density chips obtained by the array scanning system. It can filter out noise, enhance the contrast gradient of image, improve the quality of image, and implement the functions of image segmentation, edge detection and region identification. Methods The median filter method of the wavelet was used to implement the function of image denoising and improve the image quality. Edge detection was accomplished by wavelet, combining with edge operators. Image segmentation was developed by genetic algorithms. Results It could reduce the effect of spot, noise and other factors, improve the quality of image, and detect the periphery of image better and the region of sampling point more precisely. It can also effectively separate the valuable weak signal points and background or noise with the system. Conclusion The system can accomplish the functions of image pre-processing of low-density gene chips, and the adopted methods are feasible. It can provide relative accurate data information for future analysis.