Intelligent splitting in the chromosome domain
Pattern Recognition
Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations
IEEE Transactions on Pattern Analysis and Machine Intelligence
A note on the least squares fitting of ellipses
Pattern Recognition Letters
Clump splitting through concavity analysis
Pattern Recognition Letters
Analysing error of fit functions for ellipses
Pattern Recognition Letters
Direct Least Square Fitting of Ellipses
IEEE Transactions on Pattern Analysis and Machine Intelligence
Solutions to Weighted Least Squares Problems by Modified Gram-Schmidt with Iterative Refinement
ACM Transactions on Mathematical Software (TOMS)
Shape Detection in Computer Vision Using the Hough Transform
Shape Detection in Computer Vision Using the Hough Transform
Digital Image Processing
Nonparametric Segmentation of Curves into Various Representations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Morphological Image Analysis: Principles and Applications
Morphological Image Analysis: Principles and Applications
Globally Minimal Surfaces by Continuous Maximal Flows
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image Processing, Analysis, and Machine Vision
Image Processing, Analysis, and Machine Vision
A rule-based approach for robust clump splitting
Pattern Recognition
Touching Cells Splitting by Using Concave Points and Ellipse Fitting
DICTA '08 Proceedings of the 2008 Digital Image Computing: Techniques and Applications
Clump splitting via bottleneck detection and shape classification
Pattern Recognition
Edge curvature and convexity based ellipse detection method
Pattern Recognition
Computer Methods and Programs in Biomedicine
A precise ellipse fitting method for noisy data
ICIAR'12 Proceedings of the 9th international conference on Image Analysis and Recognition - Volume Part I
Guaranteed ellipse fitting with the sampson distance
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part V
Segmentation of clustered nuclei based on curvature weighting
Proceedings of the 27th Conference on Image and Vision Computing New Zealand
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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A new touching cells splitting algorithm based on concave points and ellipse fitting is proposed in this paper. The algorithm includes two parts: contour pre-processing and ellipse processing. The purpose of contour pre-processing is to smooth fluctuations of the contour, find concave points of the contour and divide the contour into different segments via the concave points. The purpose of ellipse processing is to process the different segments of the contour into possible single cells by using the properties of the fitted ellipses. Because concave points divide the whole contour of touching cells into different segments and different segments of one single cell have similar properties, the ellipse processing can separate the touching cells through ellipse fitting. This paper demonstrates a new way of using ellipse fitting to split the binary contour of touching cells. Experimental results show that our algorithm is efficient.