Segmentation of cells and sub-cellular structures (e.g., organelles) is a frequent intermediate step in cell biology and pathology. In summary, differential geometry can substantially improve the outcome of machine learning since it can enrich the underlying feature space with new geometrical invariant objects. Obtained results indicate that feature space enrichment properly balanced with feature selection functionality can achieve performance comparable to deep learning approaches. The approach is exemplified using the HeLa and HEp-2 data sets. As a second application we demonstrate whole image classification functionality based on the same principles. The approach in image segmentation is exemplified on the ISBI 2012 image segmentation challenge data set. The platform integrates expert domain knowledge, providing partial ground truth, with geometrical feature extraction based on multi-scale signal processing combined with machine learning. The article presents an automated image segmentation and classification platform, called Active Segmentation, which is based on ImageJ. However, machine learning only can not address the question as to which features are appropriate for a certain classification problem. On the other hand, machine learning offers an alternative paradigm where predefined features are combined into different classifiers, providing pixel-level classification and segmentation. Traditional image segmentation algorithms are problem-specific and limited in scope. Image segmentation still represents an active area of research since no universal solution can be identified.
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