The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
It had been known the region-scalable fitting (RSF) model can handle images with intensity inhomogeneity effectively, but it depends on the position of the initial contour. In this letter, we present a scheme about the improvement on the RSF model in term of the robustness of initialization. In the process of curve evolution, we add a function to exchange the value of fitting inside and outside curve...
It had been known that the famous region scalable-fitting model can segment the images with intensity inhomogeneity effectively, but it largely depends on the position of initial contour. In this paper, an active contour model which combines region-scalable fitting energy and optimized Laplacian of Gaussian (LoG) energy is proposed for image segmentation. We first present a LoG energy term optimized...
A robust active contour model is proposed for fast image segmentation. By introducing the intensity fitting energy in a local region, the proposed model can segment the images with intensity inhomogeneity efficiently. Since the local fitting functions are computed before curve evolution, the proposed model is insensitive to initialisation and has a high segmentation efficiency. Experiments on several...
An approach for medical image segmentation based on Fuzzy C-Means (FCM) and Level Set algorithm is proposed in this paper. FCM algorithm is suitable for solving the problems of fuzzy and uncertainty in gray level images. Level Set algorithm can effectively solve the change of the topology of the curve evolution, and realize multiple-objects extraction. In this paper, first the noise is eliminated...
The aim of detecting pulmonary nodules by computer-aided diagnostic system is reduce doctors' workload. Firstly, the part of pulmonary parenchyma should be extracted from a CT image. To extract the pulmonary parenchyma's contours well, we can utilize mathematical morphology filter. Then, Snake model is used to extract suspicious nodule's contours in pulmonary parenchyma. Finally, program will eliminate...
Oncogene is a kind of inherent genes exists in humans' cells. It has been recognized as a genetic disease, if the cells activated, it can make a person carcinogenesis. So, the research of digging out the useful information from gene chip is very hot in modern society. The sample size is small, high dimension, nonlinear which causes the 'dimension disaster', so dimensionality reduction becomes the...
Image processing is an important aspect of microarray experiments. Spots segmentation meaning to distinguish the spot signals from background pixels, is a critical step in microarray image processing. After analyzing other existing means of microarray segmentation, a new method based on region growing algorithm, mathematical morphology (MM) filtering and morphological processing is presented. And...
cDNA microarray is an independent platform that offers the ability to analyze large amount of data, and an important application in the organism??s metabolism and the gene expression analysis. cDNA microarray Image analysis aims to measure the intensity of each spot in the scanned image and this intensity represents the amount of a specific gene of the studied cell. The result can directly affect...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.