The Bi-Elliptical Deformable Contour and Its Application to Automated Tongue Segmentation in Chinese Medicine (original) (raw)
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On Automated Tongue Image Segmentation in Chinese Medicine
2002
Chinese medicine is difficult due to two special factors: (1) There are a lot of pathological details on the surface of tongue, which have a large influence on edge extraction; (2) The shapes of tongue bodies captured from various diseases or persons are quite different, so they are impossible to be properly described by a predefined deformable template. To address these problems, in this paper, we propose an original technique based on the combination of a bielliptical deformable template and an active contour model, namely Bi-Elliptical Deformable Contour (BEDC). Applying our approach to clinical tongue images, the experimental results indicate that it is superior over both traditional DT (Deformable Templates) and ACM (Active Contour Model or Snakes) with respect to stability and veracity.
A shape-based framework to segmentation of tongue contours from MRI data
2010 IEEE International Conference on Acoustics, Speech and Signal Processing, 2010
DIn this paper 1 , we propose a shape-based variational framework to curve evolution for the segmentation of tongue contours from MRI mid-sagittal images. In particular, we first build a PCA model on tongue contours of different articulations of a reference speaker, and use it as shape priors. The parameters of the curve representation are then manipulated to minimize an objective function. The designed energy integrates both global and local image information. The global term extracts roughly the object in the whole image domain; while the local term improves precision inside a small neighborhood around the contour. Promising results and comparisons with other approaches demonstrate the efficiency of our new model.
Performance Evaluation Study of Active Contour Models in Medical Imaging
Over the past years image segmentation has played an important role in medical imaging. Segmented images are used for a number of applications such as computer aided surgery, treatment planning, diagnosis, study of anatomical structures and many more. Deformable models provide a general method of performing non-rigid object segmentation. They offer an attractive approach to overcome the limitations of traditional low level segmentation approaches. A snake is a flexible deformable model which can be matched to an image contour by energy minimisation. Snakes are a powerful technique in segmenting formless shapes when little or no prior knowledge about the shape is available. Rather than handcrafting a prior knowledge into the model, the shape variation is extracted from a training set by applying PCA to PDM. In this paper some familiar Active Contour models such as Snakes by Kass et al, Balloon model, Greedy snakes method, Gradient Vector Flow Snakes are compared on the basis of qualitative and quantitative measures. The GVF Snakes have the ability to move into boundary concavities whereas the Greedy Snakes, Original Snakes have problems trying to locate the contour of an object which has a boundary concavity.
Active Contour Model for Medical Applications
Handbook of Research on Natural Computing for Optimization Problems
Recent developments in medical imaging techniques have brought an entirely new research field. Medical images are frequently corrupted by inherent noise and artifacts that could make it difficult to extract accurate information, and hence compromising the quality of clinical examination. So accurate detection is one of the major problems for medical image segmentation. Snakes or Active contour method have gained wide attention in medical image segmentation for a long time. A Snake is an energy-minimizing spline that controlled by an external energy and influenced by image energy that pull it towards features such as lines and edges. One of the key difficulties with traditional active contour algorithms is a large capture range problem. The contribution of this paper is that to in-depth analysis of the existing different contour models and implementation of techniques with minor improvements that to solve the large capture range problem. The experiment results of this model attain hi...
A comparative study of deformable contour methods on medical image segmentation
Image and Vision Computing, 2008
A comparative study to review eight different deformable contour methods (DCMs) of snakes and level set methods applied to the medical image segmentation is presented. These DCMs are now applied extensively in industrial and medical image applications. The segmentation task that is required for biomedical applications is usually not simple. Critical issues for any practical application of DCMs include complex procedures, multiple parameter selection, and sensitive initial contour location. Guidance on the usage of these methods will be helpful for users, especially those unfamiliar with DCMs, to select suitable approaches in different conditions. This study is to provide such guidance by addressing the critical considerations on a common image test set. The test set of selected images offers different and typical difficult problems encountered in biomedical image segmentation. The studied DCMs are compared using both qualitative and quantitative measures and the comparative results highlight both the strengths and limitations of these methods. The lessons learned from this medical segmentation comparison can also be translated to other image segmentation domains.
Lip contour extraction using a deformable model
Proceedings 2000 International Conference on Image Processing (Cat. No.00CH37101), 2000
The use of visual information from lip movements can improve the accuracy and robustness of a speech recognition system. Accurate extraction of visual features associated with the lips is thus very important. In this paper, we present a deformable model-based technique for lip contour extraction from color lip images. The geometric lip model we proposed is able to fit different lip shapes. Our method starts by generating a probability map of the lip image using spatial fuzzy clustering. Then the optimum set of model parameters that partitions a lip probability map into lip region and non-lip region is found, such that the joint probability of the two regions is maximised. Experimental results indicate that accurate and robust lip contour extraction is possible using this approach.
This thesis explains, in detail, the various kinds of active contour models that have attracted the attention of many in the computer vision community in the recent years. It gives a detailed description of the energy formulations and the derivation of force equations using a calculus of variations method. These snake models are combined and customized for two applications: (1) detection of double edges in x-ray images of lumbar vertebrae using pressurized open DGVF snakes, and (2) fabric stain detection using statistical balloons. The detection of double edges in x-ray images of lumbar vertebrae is of prime importance in the assessment of injury or vertebral collapse, possibly due to osteoporosis or other spine pathology. Manual segmentation is prone to errors due to subjective judgment and, hence, computer vision methods, such as snakes, are an attractive alternative to providing an automatic means of segmenting the double edges. The proposed algorithm uses a pressurized open model of DGVF snakes, customized to this application. This algorithm is applied to a set of over 30 lumbar images thus far, and the double-edge detection results have been deemed promising enough to set up a quantitative measurement for the assessment of injury or vertebral collapse. The goal in the second application is the automatic quantification of stain release in fabrics, which is an important property, impacting the fabrics’ pricing in the marketplace. Of course, to quantify stain release, one must first detect and segment the stains. This thesis proposes a balloon model with embedded statistical information in order to detect and segment the stains. A set of 15 stain images are used thus far to test the algorithm with near perfect detection and segmentation results.
2014
Image segmentation is one of the substantial techniques in the field of image processing. It is vastly used for medical purposes, tracking growth of tumor for surgical planning and simulation. Active contours or snakes are used extensively for image segmentation and processing applications, particularly to locate object boundaries. Active contours are regarded as promising and vigorously researched model-based approach to computer assisted medical image analysis. However, its utility is limited due to poor convergence of concavities and small capture range. This paper shows the application of an external force that largely solves both problems. This external force is called gradient vector flow (GVF). Using several examples to show that, GVF because of its large capture range moves snakes into boundary concavities.