Chapter 4

Deformable Models and Level Sets

in Image Segmentation

Agung Alfiansyah

4.1 Introduction

Segmentation is a partitioning process of an image domain into non-overlapping connected regions that correspond to significant anatomical structures. Automated segmentation of medical images is a difficult task. Images are often noisy and usually contain more than a single anatomical structure with narrow distances between organ boundaries. In addition, the organ boundaries may be diffuse. Although medical image segmentation has been an active field of research for several decades, there is no automatic process that can be applied to all imaging modalities and anatomical structures [1].

In general, segmentation techniques can be classified into two main categories:

(a) segmentation methods that allow users to explicitly specify the desired feature and (b) algorithms where the specification is implicit. The first segmentation class considers the segmentation as a real-time interaction process between the user and the algorithm. The user is provided with the output and allowed to perform feed-back directly in order to modify the segmentation until he/she gets a satisfactory result. In the extreme case, this framework might degenerate into manual segmentation with the user forcing his/her desirable results. Some examples of this approach are the livewire segmentation algorithms [2–5]. These algorithms produce a piecewise optimal boundary representation of an object, by viewing the image as a weighted graph and finding the shortest path between consecutive specified boundary points of the user. A more recent example of this approach based on the concept of random walks is described in [6].

The majority of segmentation methods belong to the second category, where the desired result is specified implicitly. Segmentation algorithms belonging to this category include: thresholding, various contour-based and region-based segmentation

A. Alfiansyah ( )

Surya Research and Education Center Tangerang, Tangerang, Indonesia e-mail: agung.alfi[e-mail]

G. Dougherty (ed.), Medical Image Processing: Techniques and Applications, Biological

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and Medical Physics, Biomedical Engineering, DOI 10.1007/978-1-4419-9779-1 4, © Springer Science+Business Media, LLC 2011