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Active contour model, also called snake, adapts to edges in an image. A snake is defined as an energy minimizing spline – the snake's energy depends on its shape and location within the image. Problems associated with initialization and poor convergence to boundary concavities, however, have limited its utility. In this paper, we present a new external force field, named gravitation force field, for the snake model. We associate this force field with edge preserving smoothing to drive the snake for solving the problems. Our gravitation force field uses gradient values as particles to construct force field in the whole image. This force field will attract the active contour toward the edge boundary. The locations of the initial contour are very flexible, such that they can be very far away from the objects and can be inside, outside, or the mixture. The improved snake can converge toward the object boundary in a fast pace.

In order to improve the performance of mass segmentation on mammograms, an intelligent algorithm is proposed in this paper. It establishes two mass models to characterize the various masses, and the ones in the denser tissue are represented with Model I, while the ones in the fatty tissue are represented with Model II. Then, it uses iterative thresholding to extract the suspicious area, as well as the rough regions of those masses matching Model II, and applies a DWT-based technique to locate those masses matching Model I, which are hidden in the high gray-level intensity and contrast area. A region growing process restricted by Canny edge detection is subsequently used to segment the rough regions of those masses matching Model I, and finally snakes are carried out to find all the mass regions roughly extracted above. Thirty patient cases with 60 mammograms and 107 masses were used for evaluation, and the experimental result has demonstrated the algorithm's better performance over the conventional methods.

We propose a method for the 3D segmentation and representation of cortical folds with a special emphasis on the cortical sulci. These cortical structures are represented using "active ribbons". Active ribbons are built from active surfaces, which represent the median surface of a particular sulcus filled by CSF. Sulci modeling is obtained from MRI acquisitions (usually T1 images). The segmentation is performed using an automatic labeling procedure to separate gyri from sulci based on curvature analysis of the different iso-intensity surfaces of the original MRI volume. The outer parts of the sulci are used to initialize the convergence of the active ribbon from the outer parts of the brain to the interior. This procedure has two advantages: first, it permits the labeling of voxels belonging to sulci on the external part of the brain as well as on the inside (which is often the hardest point) and secondly, this segmentation allows 3D visualization of the sulci in the MRI volumetric environment as well as showing the sophisticated shapes of the cortical structures by means of isolated surfaces. Active ribbons can be used to study the complicated shape of the cortical anatomy, to model the variability of these structures in shape and position, to assist nonlinear registrations of human brains by locally controlling the warping procedure, to map brain neurophysiological functions into morphology or even to select the trajectory of an intra-sulci (virtual) endoscope.

We describe an active contour based on the elliptical Fourier series, and its application to matrix-array ultrasound. Matrix-array, or Real Time 3D (RT3D), ultrasound is a relatively new medical imaging modality that scans a 3D-volume electronically without physically moving the transducer, allowing for real-time continuous 3D imaging of the heart. With the goal of automatically tracking the heart wall, an active contour has been developed using the elliptical Fourier series to find perpendicular lines intersecting an initial contour. The neighborhood defined by these perpendiculars is mapped into a rectangular space, called a swath, whose vertical axis represents the inside-vs-outside dimension of the contour (perpendicular to the contour), and whose horizontal axis represents parametric distance along the contour (tangent to the contour). A dynamic programming technique is then used to find the optimum error function traversing the rectangle horizontally, and this error function is mapped back into image space to yield a new contour. The method does not iterate, but rather simultaneously searches for the optimum contour within a limited domain. Results are presented applying the technique to RT3D ultrasound images of in vivo hearts.

Background: Breast cancer is a common and dreadful disease in women. One in five cancers in Singaporean women is due to breast cancer. Breast health is every woman's right and responsibility. In average, every $100 spent on breast mammogram screening, an additional $33 was spent on evaluating possible false-positive results. Thermography, with its non-radiation, non-contact and low-cost basis has been demonstrated to be a valuable and safe early risk marker of breast pathology, and an excellent case management tool available today in the ongoing monitoring and treatment of breast disease. The surface temperature and the vascularization pattern of the breast could indicate breast diseases and early detection saves lives. To establish the surface isotherm pattern of the breast and the normal range of cyclic variations of temperature distribution can assist in identifying the abnormal infrared images of diseased breasts. Before these thermograms can be analyzed objectively via computer algorithm, they must be digitized and segmented. The authors present a method to segment thermograms and extract useful region from the background. Thermography could detect the presence of tumors much earlier and of much smaller size than mammography. This paper thus aims to develop an intelligent diagnostic system based on thermography for the detection of tumors in breast. Methods: We have examined about 50 normal, healthy female volunteers in Nanyang Technological University and 130 patients in Singapore General Hospital. We did the examinations for some of them continuously for two months. From these examinations, we obtained about 1000 thermograms for contact and 800 thermograms for non-contact approaches. Standard ambient conditions were observed for all examinations. The thermograms obtained were analyzed. The first step in processing these thermograms is image segmentation. Its aim is to discern the useful region from the background. In general, autonomous segmentation is one of the most difficult tasks in image processing. This step in the process determines the eventual success or failure of the analysis. In this work, two different techniques have been presented to extract the objects from the background. Results: After analyzing these thermograms and with reference to some relevant well-documented papers, we were able to classify the thermograms. The step is very useful in identifying the normal or suspected (abnormal) thermograms. A series of thermograms was studied with the help of the in-house developed computer software. On the basis of the anatomic and vascular symmetry, the surface temperature distributions of both left and right breasts were compared. The surface isotherm pattern of breasts can indicate the local metabolism and vascularity of the underlying tissues, and the change in local blood or glandular activities can be reflected in the surface temperature of breast. We evaluated the temperature distribution pattern and the menstrual cyclic variation of temperature with time. All these results can be used to detect breast cancer. Conclusion: Automatic identification of object and surface boundary of breast thermal images is a difficult and challenging task. Both the traditional snake and gradient vector flow snake failed to detect the boundary of these images successfully. In this work, a new method is proposed in conjunction with image pre-processing, image transition, image derivative, filtering and gradient vector flow snake. This novel method can easily detect the boundary of the breast thermal image with good agreement.

Magnetic Resonance Imaging (MRI), allowing in-vivo detection of lesions, is today a crucial tool for diagnosis of Multiple Sclerosis (MS). Although the detection of lesions are not sufficient for a diagnosis of MS because of similarity with patterns detected in other neurological diseases, taking into account different radiological informations, MRI findings can often yield a high degree of confidence. We used a snake based procedure for segmentation of lesion then proposing a method based on Cluster Analysis to support clinicians in the diagnosis of MS. By identifying a minimum set of significant descriptors, our algorithm can help neurologist and neuroimaging expert to distinguish MS plaques from other kinds of lesions.