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Research On The Cell Images Processing Technology Based On Multi-scale Geometry Analysis

Posted on:2013-01-24Degree:DoctorType:Dissertation
Country:ChinaCandidate:T MengFull Text:PDF
GTID:1268330425967011Subject:Communication and Information System
Abstract/Summary:PDF Full Text Request
With the coming of digital information, especially the continual development of imageprocessing technology, it enables the medical image being the powerful supplementary meansof diagnosis and treating. Simultaneously, using these image preprocessing techniques canfurther improve the quality of medical images, thus improving the veracity of diagnosis.Traditional spatial processing method and Fourier analysis method neither sparsely representsignal in high dimensional, nor effectively catch abundant texture information of images. Inorder to solve this problem, with the development of Mathematics harmonic analysis theory,Donoho et al propose the definition of multi-resolution analysis. The propose of this theorymakes it possible to seek an optimal presentation in high-dimensional space, and catchabundant image texture information by its anisotropy, and this theory has been widely used inimage denoising, enhancement, segmentation, etc. Thereby, in this thesis multi-resolutionanalysis method is introduced to the cell image pre-processing, the main contents include thefollowing aspects:Aiming at the problem that the lack of light to low contrast and the ununiformities ofgrayscale distribution, this paper present a method for image enhancement by combined withthe NSCT and Retinex model. Based on the estimation of image exposure component subimage to realize image extraction and promote the image contrast.Aiming at the problem that image may be partially blurred due to depth of field effect.This paper proposed a multi-focus image fusion method based on multiscale analysis. Itclassify coefficient and select properly fusion coefficients by establish energy histogram ofdecomposition coefficient to get clearly fusion image.Aiming at the interference caused by noise in image transmission, denoising methodsbased on multi-scale analysis tool——Curvelet、Contourlet、NSCT transform are proposed.In order to take advantage of the correlation of coefficients, a trivariate denoising model basedon NSCT transform is put forward. The denoising shrink function is derived by introducingstatistical analysis theory into denoising processing. According to sparse representation theory,the signal sparse is the main factor which affects the reconstruction precision. Therefore, adenoising method based on3-d collaborative filtering and coefficients classification isproposed at the point of improving the signal sparse. The image quality is further improved through the conversion from the plate to stereo and the classification processing. Consideringthat the cell information is usually stored as video signal, a video denoising method based onSurfacelet transform which combines with Context model is put forward. The video signal isregarded as3-d signal to be processed and stereo multi-scale analysis is applied to it for noiseremoval.The traditional segmentation methods applied to cell images may lead the problems,such as slow speed, low accuracy of edge segmentation and segmentation error. This paperproposes two-dimensional Otsu method based on particle swarm and HMT model onContourlet domain and improved context structure image segmentation algorithm respectively,and it achieves accurate and fast segmentation contour.Finally, this paper discusses cell image recognition counting method based on the pulsecoupled neural network aiming at the identification and segmentation of cell morphology inthe research of biological cell structure and morphological change, it uses PCNN crossentropy automatic features and forward propagation and back propagation characteristics,proposing a cells image recognition counting method of dividing based on simplified PCNNmodel.
Keywords/Search Tags:image processing, cell images, multi-scale geometry analysis, sparse denoising, multi-focus fusion, image segmentation, intelligent computing
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