Accuracy Evaluation for Region Centroid-Based Registration of Fluorescent CLSM Imagery
- PDF / 2,174,564 Bytes
- 11 Pages / 600.03 x 792 pts Page_size
- 16 Downloads / 162 Views
Accuracy Evaluation for Region Centroid-Based Registration of Fluorescent CLSM Imagery Sang-Chul Lee,1 Peter Bajcsy,1 Amy Lin,2 and Robert Folberg2 1 The
National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA of Pathology, The University of Illinois Cancer Center, University of Illinois at Chicago, Chicago, IL 60607, USA
2 Department
Received 1 March 2005; Revised 30 September 2005; Accepted 16 November 2005 We present an accuracy evaluation of a semiautomatic registration technique for 3D volume reconstruction from fluorescent confocal laser scanning microscope (CLSM) imagery. The presented semiautomatic method is designed based on our observations that (a) an accurate point selection is much harder than an accurate region (segment) selection for a human, (b) a centroid selection of any region is less accurate by a human than by a computer, and (c) registration based on structural shape of a region rather than based on intensity-defined point is more robust to noise and to morphological deformation of features across stacks. We applied the method to image mosaicking and image alignment registration steps and evaluated its performance with 20 human subjects on CLSM images with stained blood vessels. Our experimental evaluation showed significant benefits of automation for 3D volume reconstruction in terms of achieved accuracy, consistency of results, and performance time. In addition, the results indicate that the differences between registration accuracy obtained by experts and by novices disappear with the proposed semiautomatic registration technique while the absolute registration accuracy increases. Copyright © 2006 Hindawi Publishing Corporation. All rights reserved.
1.
INTRODUCTION
The problem of 3D volume reconstruction can be found in multiple application domains, such as medicine, mineralogy, or surface material science. In almost all applications, the overarching goal is to automate a 3D volume reconstruction process while achieving at least the accuracy of a human operator. The benefits of automation include not only the cost of human operators but also the improved consistency of reconstruction and the eliminated training time of operators. Thus, in this paper, we study the performance of fully automatic, semiautomatic, and manual 3D volume reconstruction methods in a medical domain [1]. Specifically, we conduct experiments with fluorescent confocal laser scanning microscope imagery used for mapping the distribution of extracellular matrix proteins in serial histological sections of uveal melanoma [2, 3]. In general, a feature-based 3D volume reconstruction without a priori information requires performing the following steps. First, select a reference coordinate system or a reference image. Second, determine location of salient features in multiple data sets. This step is also denoted as finding spatial correspondences. Third, select a registration transformation model that will compensate for geometric distortions. Fourth, evaluate registr
Data Loading...
