• Journal of Biomedical Engineering Research
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• v.31 no.2
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• pp.134-140
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• 2010

Readily available high resolution brain MRI scans allow detailed visualization of the brain structures. Researchers have focused on developing methods to quantify shape differences specific to diseased scans. We have developed a novel method to quantify shape information for a specific population based on Multidimensional scaling(MDS). MDS is a well known tool in statistics and here we apply this classical tool to quantify shape change. Distance measures are required in MDS which are computed from pair-wise image registrations of the training set. Registration step establishes spatial correspondence among scans so that they can be compared in the same spatial framework. One benefit of our method is that it is quite robust to errors in registrations. Applying our method to 13 brain MRI showed clear separation between normal and diseased (Cushing's syndrome). Intentionally perturbing the image registration results did not significantly affect the separability of two clusters. We have developed a novel method to quantify shape based on MDS, which is robust to image mis-registration.

• Journal of KIISE:Software and Applications
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• v.33 no.3
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• pp.335-343
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• 2006

This paper proposes a novel technique of marker-based 2D-3D registration to combine 3D information obtained from preoperative CT images into 2D image obtained from intraoperative x-ray fluoroscopy image. Our method is divided into preoperative and intraoperative procedures. In preoperative procedure, we generate CT-derived DRRs using graphics hardware and detect markers automatically. In intraoperative procedure, we propose a hierarchical two- step registration to reduce a degree of freedom from 6-DOP to 2-DOF which is composed of in-plane registration using principal axis method and out-plane registration using minimal error searching method in spherical coordinate. For experimentation, we use cardiac phantom datasets with confirmation markers and evaluate our method in the aspects of visual inspection, accuracy and processing time. As experimental results, our method keeps accuracy and aligns very fast by reducing real-time computations.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.3
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• pp.287-293
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• 2012

This paper presents an efficient image registration method by measuring the similarity, which is based on multi-dimensional intensity fluctuation. Multi-dimensional intensity which considers 4 directions of the image, is applied to reflect more properties in similarity decision. And an intensity fluctuation is also applied to measure comprehensively the similarity by considering a change in brightness between the adjacent pixels of image. The normalized cross-correlation(NCC) is calculated by considering an intensity fluctuation to each of 4 directions. The 5 correlation coefficients based on the NCC have been used to measure the registration, which are total NCC, the arithmetical mean and a simple product on the correlation coefficient of each direction and on the normalized correlation coefficient by the maximum NCC, respectively. The proposed method has been applied to the problem for registrating the 22 face images of 243*243 pixels and the 9 person images of 500*500 pixels, respectively. The experimental results show that the proposed method has a superior registration performance that appears the image properties well. Especially, the arithmetical mean on the correlation coefficient of each direction is the best registration measure.