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In the existing various kinds of active contour model methods about image segmentation, level set method has been widely used because of its powerful capabilities of topological transformation. Because of the global computation, level set has accurate shape description effect. On the other hand, it makes the segmentation result sensitive to noise. In this paper, we propose a simple modified method...
Biomedical images are usually corrupted by strong noise and intensity inhomogeneity simultaneously. Existing region-based active contour models (RACMs) easily fail when segmenting such images. In the frequency domain, we propose a generalized RACM that presents a new way to understand the essence of classical RACMs whose segmentation results are determined by a frequency filter to extract the proposed...
Computed tomography (CT) technology is a classic medical diagnostic tool. The extraction of ts image information has great clinical significance. With the study of CT image boundary contour extraction, an improved OTSU's method which combines adaptive threshold segmentation with edge detection is invented. This method can calculate the optimal threshold automatically, simply and quickly. And the extracted...
In air/oil lubrication system, the flow regime and oil film thickness are significant for lubrication efficiency and can only be monitored online. Electrical capacitance tomography (ECT) provides a promising strategy for monitoring air/oil two phase flow in the pipelines by reconstructing cross sectional oil distributions with good real-time performance. While the low spatial resolution of ECT reconstructions...
In this paper, we propose a competitive learning approach to image segmentation by coupling the Mumford-Shah (MS) model and the Distance Sensitive Rival Penalized Competitive Learning (DSRPCL) mechanism, being denoted as the DBMS model. Actually, the DBMS model with the evolution of the level set function can get highly accurate segmentation of the image by automatically detecting the appropriate...
In iris recognition systems, iris localization is a critical step which affects the further results definitively. Most of the traditional localization methods were time consuming and sensitive to noises. To solve the problems, we propose an algorithm which adopts a momentum based level set method to locate the pupil boundary. This method hasan advantage of decreasing the effect of local optima solutions...
Active contours yield segmentation results which depend on an initial empirical parameterization stage. The latter is a tedious and time-consuming process that requires technical skills from the end user. Automated adjustment of active contour parameters is still a challenging issue. This survey reviews state-of-the-art active contours which attempt to cope with the issue of empirical parameterization,...
Objectives: Change the initial value of CV model to achieve the purpose of cell image segmentation fast and accurately. Material and methods: This paper selects slice image of cervical-cancer cells under a microscope as experimental materials. First, the original image is bilateral filtered and then the image is preprocessed using Otsu method to get the rough contour of cytoplasm. Then use Otsu method...
We describe a novel variational level set based method for delineating the sub thalamic nucleus (STN) region of human brains from magnetic resonance (MR) images. Based on the understanding of specific imaging characteristic of STN, we apply a narrow band limitation in the region-based energy function for localizing the STN from initial contour information provided by doctors. The validity of the algorithm...
We present a new object segmentation method that is based on active contours with combined saliency map.It is known that using saliency region can easily get the approximately location of the desired object in the map.In this paper,we use the saliency map to distinguish the desired object from the image when the background is full of noise,and then,to ensure the initial evolving curve in the active...
In this paper, we propose an unconstrained active contour model combining edge and region information for image segmentation. The new method achieves the segmentation by alternating the regularization term and the data-fidelity term. We use a morphological approach to the regularization term which is the most time-consuming in the energy function. The proposed method is robust to noise and avoids...
We introduce a morphological approach to curve evolution. The differential operators used in the standard PDE snake models can be approached using morphological operations on a binary level set. By combining the morphological operators associated to the PDE components we achieve a new snakes evolution algorithm. This new solution is based on numerical methods which are very simple, fast and stable...
The microscopic slice image segmentation of the interacting tissues between locust and bio-pesticide is very important in aspects of illuminating the interactive processes between the locust organs and the bio-pesticide, revealing the infective mechanism of the bio-pesticide to locust, and optimizing the biological agriculture chemical preparation. The classic image segmentation algorithms, such as...
A method based on sublevel sets is presented for refining segmentation of screening mammograms. Initial segmentation is provided by an adaptive pyramid (AP) scheme which is viewed as seeding of the final segmentation by sublevel sets. Performance is tested with and without prior anisotropic smoothing and is compared to refinement based on component merging. The combination of anisotropic smoothing,...
Prostate cancer is the second leading cause of cancer death in American men. Current prostate MRI can benefit from automated tumor localization to help guide biopsy, radiotherapy and surgical planning. An important step of automated prostate cancer localization is the segmentation of the prostate. In this paper, we propose a fully automatic method for the segmentation of the prostate. We firstly apply...
Currently in orthopedic research, bone shape variability within a specific population has been seldom investigated and used to optimise implant design, which is commonly performed by evaluating implant bone fitting on a limited dataset. In this paper, we extend our method for optimisation in statistical shape space, to global assessment of population-specific implant bone fitting. The method is based...
As an important step for iris recognition, iris segmentation provides available texture region for subsequent processing. When using portable image capture device, non-ideal iris images are often obtained. Hence, this paper presents a novel algorithm for accurate and fast iris segmentation. By combining level set theory with variational method, probabilistic active contour model (PAC) is established...
The large amount of data produced by biological live cell imaging studies of cell behavior requires accurate automated cell segmentation algorithms for rapid, unbiased and reproducible scientific analysis. This paper presents a new approach to obtain precise boundaries of cells with complex shapes using ridge measures for initial detection and a modified geodesic active contour for curve evolution...
Traditional mean shift algorithm requires a symmetrical kernel, such as a circle or an ellipse, and assumes the kernel represents the object shape. Because the symmetrical kernel always contains some background regions, the performance of moving object tracking is dramatically affected when background is complex and changes greatly. To address above issue, this paper proposes an improved mean shift...
Liver segmentation on computed tomography (CT) images is a challenging task due to the anatomic complexity and the imaging system noise. In this paper, we develop an improved level set segmentation method. Our region-based level-set approach has many advantages over the conventional active contour models. First, the improved model can get much smoother contour by adding a signed distance preserving...
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