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Heinrich, M.P.* ; Jenkinson, M.* ; Bhushan, M.* ; Matin, T.* ; Gleeson, F.V.* ; Brady, S.M.* ; Schnabel, J.A.*

MIND: Modality independent neighbourhood descriptor for multi-modal deformable registration.

Med. Image Anal. 16, 1423-1435 (2012)
DOI PMC
Open Access Green as soon as Postprint is submitted to ZB.
Deformable registration of images obtained from different modalities remains a challenging task in medical image analysis. This paper addresses this important problem and proposes a modality independent neighbourhood descriptor (MIND) for both linear and deformable multi-modal registration. Based on the similarity of small image patches within one image, it aims to extract the distinctive structure in a local neighbourhood, which is preserved across modalities. The descriptor is based on the concept of image self-similarity, which has been introduced for non-local means filtering for image denoising. It is able to distinguish between different types of features such as corners, edges and homogeneously textured regions. MIND is robust to the most considerable differences between modalities: non-functional intensity relations, image noise and non-uniform bias fields. The multi-dimensional descriptor can be efficiently computed in a dense fashion across the whole image and provides point-wise local similarity across modalities based on the absolute or squared difference between descriptors, making it applicable for a wide range of transformation models and optimisation algorithms. We use the sum of squared differences of the MIND representations of the images as a similarity metric within a symmetric non-parametric Gauss-Newton registration framework. In principle, MIND would be applicable to the registration of arbitrary modalities. In this work, we apply and validate it for the registration of clinical 3D thoracic CT scans between inhale and exhale as well as the alignment of 3D CT and MRI scans. Experimental results show the advantages of MIND over state-of-the-art techniques such as conditional mutual information and entropy images, with respect to clinically annotated landmark locations. © 2012 Elsevier B.V.
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Publication type Article: Journal article
Document type Scientific Article
Keywords Multi-modal Similarity Metric ; Non-local Means ; Non-rigid Registration ; Pulmonary Images ; Self-similarity
Language english
Publication Year 2012
HGF-reported in Year 2012
ISSN (print) / ISBN 1361-8415
e-ISSN 1361-8415
Journal Medical Image Analysis
Quellenangaben Volume: 16, Issue: 7, Pages: 1423-1435 Article Number: , Supplement: ,
Publisher Elsevier
Reviewing status Peer reviewed
Institute(s) Institute for Machine Learning in Biomed Imaging (IML)
POF-Topic(s) 30205 - Bioengineering and Digital Health
Research field(s) Enabling and Novel Technologies
PSP Element(s) G-507100-001
DOI 10.1016/j.media.2012.05.008
Scopus ID 84866457193
PubMed ID 22722056
Erfassungsdatum 2022-09-06
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