Image Segmentation Using Gray‐Scale Morphology and Marker‐ControlledWatershed Transformation

K Parvati,M Mariya Das,B S Prakasa Rao

doi:10.1155/2008/384346

Abstract

Segmentation, a new method, for color, gray‐scale MR medical images, and aerial images, is proposed. The method is based on gray‐scale morphology. Edge detection algorithm includes function edge and marker‐controlled watershed segmentation. It features the simple algorithm implemented in MATLAB. The watershed segmentation has been proved to be a powerful and fast technique for both contour detection and region‐based segmentation. In principle, watershed segmentation depends on ridges to perform a proper segmentation, a property that is often fulfilled in contour detection where the boundaries of the objects are expressed as ridges. For region‐based segmentation, it is possible to convert the edges of the objects into ridges by calculating an edge map of the image. Watershed is normally implemented by region growing, based on a set of markers to avoid oversegmentation.

Highlights

Introduction and backgroundMathematical morphology MM 1 is a nonlinear branch of the signal processing field and concerns the application of set theory concepts to image analysis
Morphology refers to the study of shapes and structures from a general scientific perspective
The standard watershed segmentation algorithm is applied on the gradient images after imposing local minima

Summary

Introduction

Mathematical morphology MM 1 is a nonlinear branch of the signal processing field and concerns the application of set theory concepts to image analysis. Morphology refers to the study of shapes and structures from a general scientific perspective. Morphological filters or operators are nonlinear transformations, which modify geometric features of images. These operators transform the original image into another image through the iteration with other image of a certain shape and size which is known as structuring element. A systematic introduction of theoretical foundations of mathematical morphology, its main image operations, and their applications can be found in 2–4. The structuring element is a set that describes a simple shape that probes an input image.

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