Low-level segmentation of aerial images with fuzzy clustering
IEEE Transactions on Systems, Man and Cybernetics
Fuzzy connectivity and mathematical morphology
Pattern Recognition Letters
Active shape models—their training and application
Computer Vision and Image Understanding
Markov random field modeling in computer vision
Markov random field modeling in computer vision
Intelligent scissors for image composition
SIGGRAPH '95 Proceedings of the 22nd annual conference on Computer graphics and interactive techniques
Graphical Models and Image Processing
Region Competition: Unifying Snakes, Region Growing, and Bayes/MDL for Multiband Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computer Vision and Image Understanding
User-steered image segmentation paradigms: live wire and live lane
Graphical Models and Image Processing
Scale-based fuzzy connected image segmentation: theory, algorithms, and validation
Computer Vision and Image Understanding - Special issue on analysis of volumetric image
Multiseeded Segmentation Using Fuzzy Connectedness
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computer Vision and Image Understanding
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast Approximate Energy Minimization via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Integration of Image Segmentation Maps using Region and Edge Information
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automated 3D Segmentation Using Deformable Models and Fuzzy Affinity
IPMI '97 Proceedings of the 15th International Conference on Information Processing in Medical Imaging
Hybrid Segmentation of Anatomical Data
MICCAI '01 Proceedings of the 4th International Conference on Medical Image Computing and Computer-Assisted Intervention
Fuzzy-connected 3D image segmentation at interactive speeds
Graphical Models
Bayesian color image segmentation using reversible jump Markov chain Monte Carlo
Bayesian color image segmentation using reversible jump Markov chain Monte Carlo
Multiscale gradient watersheds of color images
IEEE Transactions on Image Processing
Iterative relative fuzzy connectedness for multiple objects with multiple seeds
Computer Vision and Image Understanding
Three-dimensional segmentation of tumors from CT image data using an adaptive fuzzy system
Computers in Biology and Medicine
Note: Intensity standardization simplifies brain MR image segmentation
Computer Vision and Image Understanding
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This paper presents an extension of previously published theory and algorithms for fuzzy-connected image segmentation. In this approach, a strength of connectedness is assigned to every pair of image elements. This is done by finding the strongest among all possible connecting paths between the two elements in each pair. The strength assigned to a particular path is defined as the weakest affinity between successive pairs of elements along the path. Affinity specifies the degree to which elements hang together locally in the image. A scale is determined at every element in the image that indicates the size of the largest homogeneous hyperball region centered at the element. In determining affinity between any two elements, all elements within their scale regions are considered. This method has been effectively utilized in several medical applications. In this paper, we generalize this method from scalar images to vectorial images. In a vectorial image, scale is defined as the radius of the largest hyperball contained in the same homogeneous region under a predefined condition of homogeneity of the image vector field. Two different components of affinity, namely homogeneity-based affinity and object-feature-based affinity, are devised in a fully vectorial manner. The original relative fuzzy connectedness algorithm is utilized to delineate a specified object via a competing strategy among multiple objects. We have presented several studies to evaluate the performance of this method based on simulated MR images, 20 clinical MR images, and 250 mathematical phantom images. These studies indicate that the fully vectorial fuzzy connectedness formulation has generally overall better accuracy than the method using some intermediate ad hoc steps to fit the vectorial image to a scalar fuzzy connectedness formulation, and precision and efficiency are similar for these two methods.