Paper Info
Title
Development and validation of a multi-step approach to improved detection of 3D point landmarks in tomographic images
Abstract
We introduce a novel multi-step approach to improved detection of 3D anatomical point landmarks in tomographic images. Such landmarks serve as important image features for a variety of 3D medical image analysis tasks (e.g. image registration). Existing approaches to landmark detection, however, often suffer from a rather large number of false detections. Our multi-step approach combines an existing robust 3D detection operator with two different novel approaches to the reduction of false detections, and is applied within a semi-automatic procedure allowing for interactive control by the user. Experimental results obtained for a number of different anatomical landmarks of the human head in 3D CT and MR images demonstrate that both automatic ROI size selection and incorporation of a priori knowledge of the intensity structure at a landmark significantly improve the detection performance. The applicability of semi-automatic landmark extraction is thus considerably improved. We also summarize the results of a validation study in which we compare the performance of semi-automatic landmark extraction with that of a (standard) manual procedure for landmark extraction. As an exemplary application, we consider rigid MR/CT registration. The main result of our study is that compared to a purely manual procedure, semi-automatic landmark extraction (a) significantly reduces the elapsed time for landmark extraction, (b) generally yields registration results of comparable quality, and (c) increases the reproducibility of the results.
Year
DOI
Venue
2005
10.1016/j.imavis.2005.05.019
Image Vision Comput.
Keywords
Field
DocType
landmark extraction,semi-automatic landmark extraction,anatomical point landmarks,detection operator,validation,multi-step approach,detection performance,different anatomical landmark,differential approaches,landmark detection,false detection,manual procedure,improved detection,tomographic image,anatomical point landmark,a priori knowledge,image features,image registration
Computer vision,Pattern recognition,Feature (computer vision),Interactive control,A priori and a posteriori,Artificial intelligence,Landmark,Anatomical point,Mathematics,Image registration,Human head
Journal
Volume
Issue
ISSN
23
11
Image and Vision Computing
Citations 
PageRank 
References 
9
0.85
21
Authors
3
Name
Order
Citations
PageRank
Sönke Frantz1768.01
Karl Rohr237752.96
H. Siegfried Stiehl351667.10