Deformable multi-modal registration using 3D-FAST conditioned mutual information

Liu, Xueli; Tang, Zhixian; Wang, Manning; Song, Zhijian

doi:10.1080/24699322.2017.1389408

Cited by 3 publications

(1 citation statement)

References 27 publications

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“…Multimodality medical image registration is a challenging problem commonly faced in image-guided procedures and radiotherapy planning. The main difficulty of multimodality image registration is the variability of tissue or organ appearance when imaged with different physical principles, which results in the lack of a general rule to establish structure correspondence [7]. The development of advanced medical imaging technologies have resulted the provision of high resolution imaging data, which help clinicians in imageguided procedures and radiotherapy planning.…”

Section: Challenges With Multimodality Image Registrationmentioning

confidence: 99%

Challenges and Solutions in Multimodal Medical Image Subregion Detection and Registration

Alam

Rahman

2019

Journal of Medical Imaging and Radiation Sciences

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Background: The automatic detection of common subregions and registration in multimodal functional and structural images is challenging. This article gives an overview of multimodal image registration and the developments and technical issues with automatic detection and registration of subregions of interest in multimodal images. Discussion: The available knowledge about subregion detection and registration in multimodal images are described in detail. Besides the provision of compact knowledge on subregion detection and registration, the challenges and proposed solutions are also discussed. Conclusion: This article provides research guidelines for the development of automatic detection and registration of subregions of interest in functional and structural images with high accuracy and efficiency. R ESUM E Contexte : La d etection et la superposition automatiques des sousr egions communes dans les images fonctionnelles et structurelles multimodales restent un probl eme difficile. Les connaissances actuelles sur la d etection et la superposition des sous-r egions int eress ees en imagerie multimodale sont d ecrites de façon d etaill ee. M ethodologie : Outre la fourniture d'information compacte sur la d etection et la superposition des sous-r egions int eress ees, cet examen traite egalement des d efis et propose des solutions. Conclusion : Le but de cette etude et de fournir des orientations de recherche pour le d eveloppement de la d etection et du calage automatis es des sous-r egions en imagerie fonctionnelle et structurelle, avec une pr ecision et une efficacit e elev ees.

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Section: Challenges With Multimodality Image Registrationmentioning

confidence: 99%