Adaptive Segmentation of MR Axial Brain Images Using Connected Components
IWVF-4 Proceedings of the 4th International Workshop on Visual Form
Methods and Criteria for Detecting Significant Regions in Medical Image Analysis
ISMDA '01 Proceedings of the Second International Symposium on Medical Data Analysis
On the Strong Property of Connected Open-Close and Close-Open Filters
DGCI '02 Proceedings of the 10th International Conference on Discrete Geometry for Computer Imagery
The Strong Property of Morphological Connected Alternated Filters
Journal of Mathematical Imaging and Vision
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This paper discusses the behavior of some image filtering and segmentation approaches, focusing on techniques that belongs to mathematical morphology. In particular, this works studies some morphological operators and filters that consider connectivity in a special way and that, therefore, satisfactorily preserve the significant shapes and contours of an input image. Such morphological connected filters compare favorably to other filtering techniques that attempt to preserve shapes, such as, for example, anisotropic filtering or morphological non-connected filtering. Some locality and adjacency relationships are satisfied by openings and closings by reconstruction, the ``building'' pieces of the filter by reconstruction class. In addition, the composition properties of some filters by reconstruction make them suitable for multi-scale image representation. The extension of the connect filtering philosophy to the image segmentation problem achieves segmentation methods that avoid the so called resolution problem that affects some techniques. Some examples are shown that illustrate the ideas described in the paper.