Chan-Vese Active Contour Model Research Articles

Target region location based on texture analysis and active contour model

Traditional texture region location methods with Gabor features are often limited in the selection of Gabor filters and fail to deal with the target which contains both texture and non-texture parts. Thus, to solve this problem, a two-step new model was proposed. In the first step, the original features extracted by Gabor filters are applied to training a self-organizing map (SOM) neural network and a novel merging scheme is presented to achieve the clustering. A back propagation (BP) network is used as a classifier to locate the target region approximately. In the second step, Chan-Vese active contour model is applied to detecting the boundary of the target region accurately and morphological processing is used to create a connected domain whose convex hull can cover the target region. In the experiments, the proposed method is demonstrated accurate and robust in localizing target on texture database and practical barcode location system as well.

Automatic tracing and segmentation of rat mammary fat pads in MRI image sequences based on cartoon-texture model

The growth patterns of mammary fat pads and glandular tissues inside the fat pads may be related with the risk factors of breast cancer. Quantitative measurements of this relationship are available after segmentation of mammary pads and glandular tissues. Rat fat pads may lose continuity along image sequences or adjoin similar intensity areas like epidermis and subcutaneous regions. A new approach for automatic tracing and segmentation of fat pads in magnetic resonance imaging (MRI) image sequences is presented, which does not require that the number of pads be constant or the spatial location of pads be adjacent among image slices. First, each image is decomposed into cartoon image and texture image based on cartoon-texture model. They will be used as smooth image and feature image for segmentation and for targeting pad seeds, respectively. Then, two-phase direct energy segmentation based on Chan-Vese active contour model is applied to partitioning the cartoon image into a set of regions, from which the pad boundary is traced iteratively from the pad seed. A tracing algorithm based on scanning order is proposed to accurately trace the pad boundary, which effectively removes the epidermis attached to the pad without any post processing as well as solves the problem of over-segmentation of some small holes inside the pad. The experimental results demonstrate the utility of this approach in accurate delineation of various numbers of mammary pads from several sets of MRI images.

Real-Time Visual Tracking Using Geometric Active Contours and Particle Filters

Image processing and flltering are essential elements for the visual tracking system in an unmanned aerial vehicle. This paper presents visual tracking methodology based on particle flltering in the framework of a fast implementation of the Chan-Vese active contour model. The fast implementation greatly improves the computational time of the segmentation process. We have tested particle flltering using this fast active contour model, and the flltering algorithm has shown the ability to robustly track an aerial target under varied conditions. The computational speed of the algorithm has allowed us to employ it for formation ∞ight among UAVs. We have also demonstrated the utility of the flltering algorithm for multiple target tracking in the presence of occlusions.