Medical Image Analysis: Progress over Two Decades and the Challenges Ahead
IEEE Transactions on Pattern Analysis and Machine Intelligence
Watersnakes: Energy-Driven Watershed Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Segmentation of Blood Images Using Morphological Operators
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 3
The Viscous Watershed Transform
Journal of Mathematical Imaging and Vision
Image Analysis and Mathematical Morphology
Image Analysis and Mathematical Morphology
A Locally Constrained Watershed Transform
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image and Vision Computing
Watershed segmentation using prior shape and appearance knowledge
Image and Vision Computing
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The segmentation of medical images poses a great challenge in the area of image processing and analysis due mainly to noise, complex background, fuzzy and overlapping objects, and nonhomogeneous gradients. This work uses the so-called locally constrained watershed transform introduced by Beare [1] to address these problems. The shape constraints introduced by this type of flexible watershed transformation permit to successfully segment and separate regions of interest. This type of watershed offers an alternative to other methods (such as distance function flooding) for particle extraction in medical imaging segmentation applications, where particle overlapping is quite common. Cytology images have been used for the experimental results.