Machine Learning in Morphological Segmentation

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Author(s): O. Lezoray (Universite de Caen Basse-Normandie, France), G. Lebrun (Universite de Caen Basse-Normandie, France), C. Meurie (INRETS-LEOST, France), C. Charrier (Universite de Caen Basse-Normandie, France), A. Elmotataz (Universite de Caen Basse-Normandie, France)and M. Lecluse (Centre Hospitalier Public du Cotentin, France)
Copyright: 2012
Pages: 15
Source title: Machine Learning: Concepts, Methodologies, Tools and Applications
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-60960-818-7.ch309

Keywords: Artificial Intelligence / Computational Intelligence / Computer Science & IT / Information Science Reference

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Abstract

The segmentation of microscopic images is a challenging application that can have numerous applications ranging from prognosis to diagnosis. Mathematical morphology is a very well established theory to process images. Segmentation by morphological means is based on watershed that considers an image as a topographic surface. Watershed requires input and marker image. The user can provide the latter but far more relevant results can be obtained for watershed segmentation if marker extraction relies on prior knowledge. Parameters governing marker extraction varying from image to image, machine learning approaches are of interest for robust extraction of markers. We review different strategies for extracting markers by machine learning: single classifier, multiple classifier, single classifier optimized by model selection.

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