Geometry-based vs. intensity-based medical image registration: A comparative study on 3D CT data

Computers in Biology and Medicine
Antonis D SavvaGeorge K Matsopoulos

Abstract

Spatial alignment of Computed Tomography (CT) data sets is often required in numerous medical applications and it is usually achieved by applying conventional exhaustive registration techniques, which are mainly based on the intensity of the subject data sets. Those techniques consider the full range of data points composing the data, thus negatively affecting the required processing time. Alternatively, alignment can be performed using the correspondence of extracted data points from both sets. Moreover, various geometrical characteristics of those data points can be used, instead of their chromatic properties, for uniquely characterizing each point, by forming a specific geometrical descriptor. This paper presents a comparative study reviewing variations of geometry-based, descriptor-oriented registration techniques, as well as conventional, exhaustive, intensity-based methods for aligning three-dimensional (3D) CT data pairs. In this context, three general image registration frameworks were examined: a geometry-based methodology featuring three distinct geometrical descriptors, an intensity-based methodology using three different similarity metrics, as well as the commonly used Iterative Closest Point algorithm. All techniqu...Continue Reading

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Citations

Mar 21, 2017·BioMed Research International·Zhiying SongGuoxu Zhang
May 11, 2019·Sensors·Yingzhong TianWenbin Wang
Feb 25, 2020·Computational and Mathematical Methods in Medicine·Jorge Perez-GonzalezVerónica Medina-Bañuelos

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