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: 2009
Pages: 15
Source title: Handbook of Research on Advanced Techniques in Diagnostic Imaging and Biomedical Applications
Source Author(s)/Editor(s): Themis P. Exarchos (University of Ioannina, Greece ), Athanasios Papadopoulos (University of Ioannina, Greece )and Dimitrios I. Fotiadis (University of Ioannina, Greece )
DOI: 10.4018/978-1-60566-314-2.ch021

Keywords: Clinical Technologies / Medical Information Science Reference / Medical Technologies / Medicine & Healthcare

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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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Machine Learning in Morphological Segmentation

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