Digital image analysis is the field of extracting relevant information from digital images. Recent developments in imaging techniques have made 3-dimensional volume images more common. This has created a need to extend existing 2D image analysis tools to handle images of higher dimensions. Such extensions are usually not straightforward. In many cases, the theoretical and computational complexity of a problem increases dramatically when an extra dimension is added.A fundamental problem in image analysis is image segmentation, i.e., identifying and separating relevant objects and structures in an image. Accurate segmentation is often required for further processing and analysis of the image can be applied. Despite years of active research, general image segmentation is still seen as an unsolved problem. This mainly due to the fact that it is hard to identify objects from image data only. Often, some high-level knowledge about the objects in the image is needed. This high-level knowledge may be provided in different ways. For fully automatic segmentation, the high-level knowledge must be incorporated in the segmentation algorithm itself. In interactive applications, a human user may provide high-level knowledge by guiding the segmentation process in various ways.The aim of the work presented here is to develop segmentation and analysis tools for volume images…
Contents
1 Introduction
2 Volume image acquisition
2.1 Computed tomography
2.2 Magnetic resonance imaging
3 Fundamental image analysis concepts
3.1 Digital images
3.2 Spatial filtering
3.2.1 Smoothing filters
3.2.2 Gradient extraction
3.3 Distance transforms
4 Image segmentation
4.1 Segmentation by thresholding
4.2 Watershed segmentation
4.3 Live-wire segmentation
4.4 Graph-cut segmentation
5 Applications
5.1 Analysis of volume images of fibrous materials
5.2 Interactive segmentation of medical volume images
6 Contributions
7 Discussion and suggestions for future work
Author: Malmberg, Filip
Source: Uppsala University Library
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