Brain Extraction Using Active Contour Neighborhood-Based Graph Cuts Model
نویسندگان
چکیده
منابع مشابه
Object contour tracking using graph cuts based active contours
In this paper, we present an object contour tracking approach using graph cuts based active contours (GCBAC). Our proposed algorithm does not need any a priori global shape model, which makes it useful for tracking objects with deformable shapes and appearances. GCBAC are not sensitive to initial conditions and always converge to the optimal contour within the dilated neighborhood of itself, Gi...
متن کاملBrain extraction from cerebral MRI volume using a hybrid level set based active contour neighborhood model
BACKGROUND The extraction of brain tissue from cerebral MRI volume is an important pre-procedure for neuroimage analyses. The authors have developed an accurate and robust brain extraction method using a hybrid level set based active contour neighborhood model. METHODS The method uses a nonlinear speed function in the hybrid level set model to eliminate boundary leakage. When using the new hy...
متن کاملAudio-Visual Object Extraction using Graph Cuts
We propose a novel method to automatically extract the audio-visual objects that are present in a scene. First, the synchrony between related events in audio and video channels is exploited to identify the possible locations of the sound sources. Video regions presenting a high coherence with the soundtrack are automatically labelled as being part of the audio-visual object. Next, a graph cut s...
متن کاملObject Boundary Segmentation Using Graph Cuts Based Active Contours
In this paper we propose an iterative graph cuts based active contours approach to segment an object boundary out of background. Given an initial boundary nearby the object, the graph cuts based active contour can iteratively deform to the object boundary even if there are large discontinuities and noise. In each iteration, the area of interest is a certain neighborhood of the previously estima...
متن کاملObject Segmentation Using Graph Cuts Based Active Contours
In this paper we present a graph cuts based active contours (GCBAC) approach to object segmentation. GCBAC approach is a combination of the iterative deformation idea of active contours and the optimization tool of graph cuts. It differs from traditional active contours in that it uses graph cuts to iteratively deform the contour and its cost function is defined as the summation of edge weights...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Symmetry
سال: 2020
ISSN: 2073-8994
DOI: 10.3390/sym12040559