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Left ventricle(LV) segmentation is a prerequisite step of evaluation of LV structure and function, which plays an important role in the diagnosis and treatment of cardiovascular diseases. In this paper, we propose a method to segment endocardium and epicardium of LV using convolution neural network combined with active contour model and tensor voting. A fully convolution neural network (FCN) named...
The measurement of residual thyroid tissue after thyroidectomy is crucial for the precise quantification of thyroid cancer treatment. Accurate residual thyroid tissue segmentation from CT images is challenging due to the indistinct tissue boundary. We propose a vote-in & vote-out region propagation model for residual thyroid tissue segmentation which incorporates global and local constraints and...
In this paper, we are proposing a 3D segmentation and interactive visualization workflow. The segmentation implementation uses a globally convex multiphase active contours without edges. This algorithm has been proven to be initialization independent due to their globally convex formulation and better than other approaches due to robustness to image variations and adaptive energy functionals. The...
According to the tensor diffusion theory, this paper proposes a novel active contour model for image segmentation in the level set formulation. Firstly, we define an external energy term using the trace-based tensor diffusion equation, which can locate the evolution curve to the neighborhood of one target boundaries adaptively. So it will enhance the robustness of the model to the initialization of...
The accurate detection of region(s)-of-interest (ROI) via Active Contour Method (ACM) is a well-known and evolving research topic in image segmentation. A novel region-based active contour method is proposed that can segment real and synthetic images with blurred borders more efficiently. Additionally, a new Signed Pressure Force (SPF) function named as Hyperbolic Trigonometric Signed Pressure Force...
Breast cancer is the most common category of cancers in woman around the world. Ultrasonography imaging modalities (US) are highly recommended for breast cancer examining due to their sensitivity, specificity, cost-effective, accessibility, portability, comfort, as well as non-invasive tool. In addition, an integration of a conventional US and its adjunct modalities which is Power Doppler has been...
Carotid artery stenosis is usually the bottleneck caused by atherosclerosis or carotid artery luminal narrowing. Carotid arteries are in close proximity to bone and bony structures as spongy. Contrast-enhanced computerized tomography angiography (CTA) is used to monitor and measure carotid arteries under the control of an expert. Recently, there is a strong and growing demand for improving the computer...
This paper proposes a novel algorithm for static and single camera foreground detection and multi-person tracking using active contour and Gaussian Mixture Model (GMM) methods. A new unsupervised multi-person re-identification algorithm has been developed, which dynamically assigns labels to persons for recognition. Detection of persons that have ever been in motion but become stationary for a long...
Cancer is the major reason for mortality worldwide. The chances of recovery can be well improved if it is possible to diagnose the cancer at its early stages. Cancer detection is conventionally done by invasive procedures like biopsy, but it causes lot of discomforts for the patient. Here Hyperspectral Images (HSI) based noninvasive alternative for biopsy is introduced, it is also applicable as a...
Active contour models (ACM) have been proven to be the most promising model in solving the different problems encountered in image segmentation. This paper proposes a new region-based active contour model for level set formulation in which the energy function is formulated using both local and global intensity fitting terms. The generalized Gaussian distribution has been used as the kernel function...
Detection and segmentation of cells is an important step for classifying the cells as cancerous or non-cancerous. Pathologists use microscopic images for analysis and further diagnosis of cancer. These images contain the microscopic structure of tissues and are stained using some staining components to facilitate the process. Staining process varies due to different stain manufacturers, staining practices...
Edge-based models can identify the objects having solid borderline effectively, however, these models cannot appropriately segment the images with weak object borderline. On the other hand, region-based models perform better for images with unclear object borderline, however, these models cannot accurately segment images with intensity inhomogeneity. In this study, we propose a hybrid Active Contour...
This paper presents a variational method for SAR image segmentation that unifies boundary- and region-based information into the geometric active contour model. A new Riemannian metric is introduced to construct region-based energy function derived by maximizing the geodesic distance of a new Riemannian metric in differential-geometric structure for spectral density functions. A heterogeneity indicator...
A novel level set-based active contour model (LSAC) composed by region and boundary terms is proposed to segment the images featured by low contrast and blurred boundaries. The region terms derived from weighted cross-entropy play major role to locate object boundary and the boundary term derived from direct detection of image gradient plays supplementary role for promotion of segmentation accuracy...
In order to classify the eaglewood, the work proposed a method of wood fiber segmentation and characteristic extraction based on the eaglewood micrographs. The active contour model was used to extract the contours of the eaglewood micrographs. After screening of wood fiber, the geometric features and shape factors were extracted to form characteristic vectors. After that, SVM was used to achieve the...
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...
The moving target detection is not only one of the key techniques in intelligent monitoring system and modern warfare but also an important field of image processing. Therefore, the key to ensuring the success of the follow-up tasks is to find the target as soon as possible. Due to the complex target background and the uncertainty of the target motion state, there are the missed detection and false...
Infrared image possess the characteristics of large noises, concentrated intensity, blurring target edge, and poor contrast between target and background, which causing the segmentation of damage targets in aircraft skin infrared thermal image become difficult. Aiming at this problem, a method of infrared image segmentation based on the game between Markov Random Field (MRF) and improved Gradient...
Traumatic and degenerative shoulder pathologies of which treatment strategy and success depends on correct diagnosis are more commonly encountered at the present time. The aim of this study is to help clinicians by using computer based decision support systems to diagnose correctly the degenerative and traumatic conditions of shoulder from MR images which is not an easy task in practice. Image patches...
Accurate localization and segmentation of an optic disk (OD) is an important problem in the analysis of abnormality conditions such as optic disk shrinking/swelling, pale optic disk and glucoma. Hence, this paper proposes an automated fast and accurate OD localization and segmentation technique. In this work, OD localization is performed using the extended feature projection method (EFP) based on...
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