Machine Learning in Morphological Segmentation

Machine Learning in Morphological Segmentation

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
DOI: 10.4018/978-1-60960-818-7.ch309
OnDemand PDF Download:
$30.00
List Price: $37.50

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.
Chapter Preview
Top

Background

Mathematical Morphology is a very well established theory to process images (Serra, 1988). The watershed is the basic tool of Mathematical Morphology for segmentation. It has proved to be a powerful tool and it is used in a large number of applications, such as, medicine, remote sensing, robotics, and multimedia (Meyer, 2001). The parameters for a watershed are marker and input images (Soille, 2004). The watershed grows the markers based on a flooding simulation process by considering the input image as a topographic surface. The problem is to produce the divide-line image on this surface (Roerdink, 2000). Each marker is associated to a color. The topography is flooded from below by letting colored water rise from the holes with its associated color, at an uniform rate across the entire image. When the rising water of distinct colors would merge, a dam is built to prevent the merging. Figure 1 illustrates such a process on a color hematology image with two different sets of markers (provided by the user or by a machine learning algorithm). The most difficult problem when using watershed is of course the definition of appropriate markers with minimal efforts (Rivest, 1992; Meyer, 2001). User provided markers can be attractive for interactive segmentation but for automatic segmentation other techniques have to be considered. An accurate extraction of reliable markers requires prior knowledge on the latter (color, texture, shape, etc.). To incorporate such prior knowledge for the automatic extraction of markers, machine-learning techniques (Derivaux, 2007; Lezoray, 2002; Levner 2007) are the most natural candidates. Figure 2 provides a schematic view of all components involved in the design of a morphological segmentation scheme relying on machine learning algorithms for marker extraction. To perform morphological color image segmentation, a machine learning based classification of pixel feature vectors is done. The result is labeled in connected components and refined by a color watershed. To infer a proper machine learning based pixel classifier, an image database with an associated ground truth is constructed and pixel feature vectors are shared among classes as a basis for supervised learning. In the following Sections, conceiving of each one of these components is described.

Complete Chapter List

Search this Book:
Reset