Challenges and Solutions in Multimodal Medical Image Subregion Detection and Registration

Abstract

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.

Recent Trends, Technical Concepts and Components of Computer-Assisted Orthopedic Surgery Systems: A Comprehensive Review

“…This type of registration is affected by any modification of parameters. Global transformation may be effectively conducted by using local parameters affected by image noise and artefacts, because it suppresses these portions of the signal as part of the image preprocessing methods which are used in the domain of registration transformation [82,83].…”

“…Depending on the number of geometrical dimensions, we distinguish 2D/2D, 2D/3D and 3D/3D registrations. Analysis of dynamic sequences of clinical images based on time series registration enables bone features to be tracked over time [83,84].…”

“…A crucial task of the optimization procedure is to select appropriate parameters which possess a high level of similarity. Finally, the obtained parameters are used to achieve the best matched image based on similarity metrics [83,85,86].…”

“…In the first step, the clinical images are obtained by a scanner (MRI or CT); the region of interest (RoI) is thus interactively selected based on the expert’s experience. In the next step, the interoperable images are matched and registered with the preoperative images [83]. The main benefit of this type of registration is the dynamic matching and tracking of the RoI.…”

Modified Global Flower Pollination Algorithm‐based image fusion for medical diagnosis using computed tomography and magnetic resonance imaging

“…Recently, the efficient, robustness, and accurate multimodal registration is important. A fusion of two modalities into a single image creates a few challenging issues 14 . The optimal landmark selection and feature extraction are complex.…”