A survey of thresholding techniques
Computer Vision, Graphics, and Image Processing
The illusion of reality
Corner detection from chain-code
Pattern Recognition
Multiresolution image shape description
Multiresolution image shape description
A Theory of Multiscale, Curvature-Based Shape Representation for Planar Curves
IEEE Transactions on Pattern Analysis and Machine Intelligence
Decomposition of digital clumps into convex parts by contour tracing and labelling
Pattern Recognition Letters
Clump splitting through concavity analysis
Pattern Recognition Letters
Parts of Visual Form: Computational Aspects
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Evaluation and comparison of different segmentation algorithms
Pattern Recognition Letters
IEEE Transactions on Pattern Analysis and Machine Intelligence
A boundary concavity code to support dominant point detection
Pattern Recognition Letters
The watershed transform: definitions, algorithms and parallelization strategies
Fundamenta Informaticae - Special issue on mathematical morphology
Low-level grouping mechanisms for contour completion
Information Sciences—Applications: An International Journal
An efficient algorithm for the optimal polygonal approximation of digitized curves
Pattern Recognition Letters
Deformable Shape Detection and Description via Model-Based Region Grouping
IEEE Transactions on Pattern Analysis and Machine Intelligence
Cell image segmentation with kernel-based dynamic clustering and an ellipsoidal cell shape model
Computers and Biomedical Research
Recognizing Planar Objects Using Invariant Image Features
Recognizing Planar Objects Using Invariant Image Features
Shape Detection in Computer Vision Using the Hough Transform
Shape Detection in Computer Vision Using the Hough Transform
Perceptual Organization and Visual Recognition
Perceptual Organization and Visual Recognition
Shape Analysis and Classification: Theory and Practice
Shape Analysis and Classification: Theory and Practice
Locating Perceptually Salient Points on Planar Curves
IEEE Transactions on Pattern Analysis and Machine Intelligence
Visualizing concave and convex partitioning of 2D contours
Pattern Recognition Letters
Yet Another Survey on Image Segmentation: Region and Boundary Information Integration
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
Scale-Space '01 Proceedings of the Third International Conference on Scale-Space and Morphology in Computer Vision
Indentation and Protrusion Detection and Its Applications
Scale-Space '01 Proceedings of the Third International Conference on Scale-Space and Morphology in Computer Vision
Fast Statistical Level Sets Image Segmentation for Biomedical Applications
Scale-Space '01 Proceedings of the Third International Conference on Scale-Space and Morphology in Computer Vision
Polygon decomposition based on the straight line skeleton
Proceedings of the nineteenth annual symposium on Computational geometry
Cell Segmentation with Median Filter and Mathematical Morphology Operation
ICIAP '99 Proceedings of the 10th International Conference on Image Analysis and Processing
An improved watershed algorithm for counting objects in noisy, anisotropic 3-D biological images
ICIP '95 Proceedings of the 1995 International Conference on Image Processing (Vol. 3)-Volume 3 - Volume 3
Semi-Automatic Segmentation of Tissue Cells from Confocal Microscope Images
ICPR '96 Proceedings of the International Conference on Pattern Recognition (ICPR '96) Volume III-Volume 7276 - Volume 7276
Morphological Segmentation of Histology Cell Images
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 1
Segmentation of Dense Leukocyte Clusters
MMBIA '01 Proceedings of the IEEE Workshop on Mathematical Methods in Biomedical Image Analysis (MMBIA'01)
ICIAP '03 Proceedings of the 12th International Conference on Image Analysis and Processing
Digital Image Processing Using MATLAB
Digital Image Processing Using MATLAB
Approaches to Decompose Flat Structuring Element for Fast Overlapping Search Morphological Algorithm
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 2 - Volume 2
Medial axis extraction and shape manipulation of solid objects using parabolic PDEs
SM '04 Proceedings of the ninth ACM symposium on Solid modeling and applications
IEEE Transactions on Computers
IEEE Transactions on Computers
Computerized cell image analysis: past, present, and future
SCIA'03 Proceedings of the 13th Scandinavian conference on Image analysis
Segmentation of cell nuclei in tissue by combining seeded watersheds with gradient information
SCIA'03 Proceedings of the 13th Scandinavian conference on Image analysis
Shape partitioning by convexity
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Segmentation of neuronal nuclei based on clump splitting and a two-step binarization of images
Expert Systems with Applications: An International Journal
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Successful segmentation of a multilevel to a bilevel microscopic cell image rather frequently gives rise to touching objects which need to be separated in order to perform object specific measurements. The standard approach of dealing with this problem is a watershed decomposition of gradient, distance or low pass filtered transforms. However, if cell clustering is excessive, the cell size varies and cells have various shapes that are different from circles the watershed approaches produce unsatisfying results. We found a technique that splits cell clumps into meaningful parts. Since this method is based on the analysis of contour curvature on the scale space of Fourier coefficients relevant dominant points can be recognized. Based on an optimized heuristic approach pairs of these dominant points are recursively matched since splitted objects do not possess concavities respectively intrusions anymore. The advantages of this approach are (i) the independence of cell shapes which are clumped, (ii) the consideration of holes or background intensities within objects, (iii) the robustness in terms of convergence and a few parameters only to adapt to other families of decomposition problems. The objective of this contribution is to explain the algorithm, show its results using different examples from benchmark databases, self generated images and complex configurations of cell images.