Pattern Recognition
Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Two-dimensional signal and image processing
Two-dimensional signal and image processing
Decomposition of digital clumps into convex parts by contour tracing and labelling
Pattern Recognition Letters
Contrast limited adaptive histogram equalization
Graphics gems IV
Clump splitting through concavity analysis
Pattern Recognition Letters
An Introduction to Digital Image Processing
An Introduction to Digital Image Processing
Image Analysis and Mathematical Morphology
Image Analysis and Mathematical Morphology
A rule-based approach for robust clump splitting
Pattern Recognition
Splitting touching cells based on concave points and ellipse fitting
Pattern Recognition
On the decomposition of cell clusters
Journal of Mathematical Imaging and Vision
Clump splitting based on detection of dominant points from contours
CASE'09 Proceedings of the fifth annual IEEE international conference on Automation science and engineering
Level set methods for watershed image segmentation
SSVM'07 Proceedings of the 1st international conference on Scale space and variational methods in computer vision
Distance regularized level set evolution and its application to image segmentation
IEEE Transactions on Image Processing
New Fusion Operations for Digitized Binary Images and Their Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
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In this paper we present an algorithm to segment the nuclei of neuronal cells in confocal microscopy images, a key technical problem in many experimental studies in the field of neuroscience. We describe the whole procedure, from the original images to the segmented individual nuclei, paying particular attention to the binarization of the images, which is not straightforward due to the technical difficulties related to the visualization of nuclei as individual objects and incomplete and irregular staining. We have focused on the division of clusters of nuclei that appear frequently in these images. Thus we have developed a clump-splitting algorithm to separate touching or overlapping nuclei allowing us to accurate account for both the number and size of the nuclei. The results presented in the paper show that the proposed algorithm performs well on different sets of images from different layers of the cerebral cortex.