Chapter 8

High-Throughput Detection of Linear Features:

Selected Applications in Biological Imaging

Luke Domanski, Changming Sun, Ryan Lagerstrom, Dadong Wang, Leanne Bischof, Matthew Payne, and Pascal Vallotton

8.1 Introduction

Psychovisual experiments support the notion that a considerable amount of information is contained in region boundaries such as edges and linear features [1]. Thus, as long as these elements are preserved, it is possible to simplify images drastically with no apparent loss of content. Linear features also underlie the organization of many structures of interest in biology, remote sensing, medicine, and engineering. Examples include rivers and their deltas, road networks, the circulatory system, and textile microstructure (see [2] for a more extensive list and Chapters 6, 7, and 11 in this book).

Given that linear features play a central role in image analysis, a major aim is to develop fast and sensitive implementations for identifying them in digital images. Section 8.2 describes an efficient approach based on nonmaximum intensity suppression. The method systematically probes the image intensity along short segments in the image. By sampling a few transect directions (typically 8) at each image pixel in turn, linear features can be detected very rapidly. Although the algorithm in its original form is unable to detect linear features in close proximity, small modifications can deal with this issue while sacrificing very little in terms of speed. This is described in Sect. 8.2.3.

The output of our linear feature detector sometimes contains artifacts that need treatment. Thus, one typically removes isolated pixels and attempts to restore skeleton continuity when the latter is broken. Our approach to these issues is presented in Sect. 8.2.4.

Linear features can be considered intermediate representations on which to apply further computations to obtain morphological information, such as the number of

P. Vallotton ( )

CSIRO (Commonwealth Scientific and Industrial Research Organisation), North Ryde, Australia e-mail: [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 8, © Springer Science+Business Media, LLC 2011