Int J Med Robot - Manifold-based feature point matching for multi-modal image registration.

Tópicos

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Resumo

CKGROUND: Images captured using different modalities usually have significant variations in their intensities, which makes it difficult to reveal their internal structural similarities and achieve accurate registration. Most conventional feature-based image registration techniques are fast and efficient, but they cannot be used directly for the registration of multi-modal images because of these intensity variations.METHODS: This paper introduces the theory of manifold learning to transform the original images into mono-modal modalities, which is a feature-based method that is applicable to multi-modal image registration. Subsequently, scale-invariant feature transform is used to detect highly distinctive local descriptors and matches between corresponding images, and a point-based registration is executed.RESULTS: The algorithm was tested with T1- and T2-weighted magnetic resonance (MR) images obtained from BrainWeb. Both qualitative and quantitative evaluations of the method were performed and the results compared with those produced previously. The experiments showed that feature point matching after manifold learning achieved more accurate results than did the similarity measure for multi-modal image registration.CONCLUSIONS: This study provides a new manifold-based feature point matching method for multi-modal medical image registration, especially for MR images. The proposed method performs better than do conventional intensity-based techniques in terms of its registration accuracy and is suitable for clinical procedures.

Resumo Limpo

ckground imag captur use differ modal usual signific variat intens make difficult reveal intern structur similar achiev accur registr convent featurebas imag registr techniqu fast effici use direct registr multimod imag intens variationsmethod paper introduc theori manifold learn transform origin imag monomod modal featurebas method applic multimod imag registr subsequ scaleinvari featur transform use detect high distinct local descriptor match correspond imag pointbas registr executedresult algorithm test t tweight magnet reson mr imag obtain brainweb qualit quantit evalu method perform result compar produc previous experi show featur point match manifold learn achiev accur result similar measur multimod imag registrationconclus studi provid new manifoldbas featur point match method multimod medic imag registr especi mr imag propos method perform better convent intensitybas techniqu term registr accuraci suitabl clinic procedur

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