• Journal of Computing Science and Engineering
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• v.6 no.1
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• pp.1-11
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• 2012

Computed tomography (CT) images are widely used for the analysis of the temporal evaluation or monitoring of the progression of a disease. The follow-up examinations of CT scan images of the same patient require a 3D registration technique. In this paper, an automatic and robust registration is proposed for the rigid registration of 3D CT images. The proposed method involves two steps. Firstly, the two CT volumes are aligned based on their principal axes, and then, the alignment from the previous step is refined by the optimization of the similarity score of the image's voxel. Normalized cross correlation (NCC) is used as a similarity metric and a downhill simplex method is employed to find out the optimal score. The performance of the algorithm is evaluated on phantom images and knee synthetic CT images. By the extraction of the initial transformation parameters with principal axis of the binary volumes, the searching space to find out the parameters is reduced in the optimization step. Thus, the overall registration time is algorithmically decreased without the deterioration of the accuracy. The preliminary experimental results of the study demonstrate that the proposed method can be applied to rigid registration problems of real patient images.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.37 no.6
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• pp.453-464
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• 2019

Recently, as the demand for 3D geospatial information increases, the importance of rapid and accurate data construction has increased. Although many studies have been conducted to register UAV (Unmanned Aerial Vehicle) imagery based on LiDAR (Light Detection and Ranging) data, which is capable of precise 3D data construction, studies using LiDAR data embedded in MMS (Mobile Mapping System) are insufficient. Therefore, this study compared and analyzed 9 matching algorithms based on feature points for registering reflectance image converted from LiDAR point cloud intensity data acquired from MMS with image data from UAV. Our results indicated that when the SIFT (Scale Invariant Feature Transform) algorithm was applied, it was able to stable secure a high matching accuracy, and it was confirmed that sufficient conjugate points were extracted even in various road environments. For the registration accuracy analysis, the SIFT algorithm was able to secure the accuracy at about 10 pixels except the case when the overlapping area is low and the same pattern is repeated. This is a reasonable result considering that the distortion of the UAV altitude is included at the time of UAV image capturing. Therefore, the results of this study are expected to be used as a basic research for 3D registration of LiDAR point cloud intensity data and UAV imagery.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.41 no.1
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• pp.55-62
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• 2004

A semi-automatic registration process of determining specified points is presented, which is required to register brain MR images based on Talairach atlas. Generally, ten specified points that define Talairach coordinates are anterior commissure(AC), posterior commissure (PC), anterior feint (AP), posterior point (PP), superior point (SP), inferior point (IP), left point (LP), right point (RP) and two points for the midline of the brain. The suggested method reduces user interaction for S points, and finds the necessary points for registration in a more stable manner by finding AC and PC using two-level shape matching of the corpus callosum (CC) in an edge-enhanced brain M image. Remaining points are found using the intensity information of cutview.

• Investigative Magnetic Resonance Imaging
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• v.19 no.3
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• pp.168-177
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• 2015

Purpose: Brain surface intensity model (BSIM)-based cortical thickness analysis does not require complicated 3D segmentation of brain gray/white matters. Instead, this technique uses the local intensity profile to compute cortical thickness. The aim of the present study was to evaluate intra-rater and inter-rater reliability of BSIM-based cortical thickness analysis using images from elderly participants. Materials and Methods: Fifteen healthy elderly participants (ages, 55-84 years) were included in this study. High-resolution 3D T1-spoiled gradient recalled-echo (SPGR) images were obtained using 3T MRI. BSIM-based processing steps included an inhomogeneity correction, intensity normalization, skull stripping, atlas registration, extraction of intensity profiles, and calculation of cortical thickness. Processing steps were automatic, with the exception of semiautomatic skull stripping. Individual cortical thicknesses were compared to a database indicating mean cortical thickness of healthy adults, in order to produce Z-score thinning maps. Intra-class correlation coefficients (ICCs) were calculated in order to evaluate inter-rater and intra-rater reliabilities. Results: ICCs for intra-rater reliability were excellent, ranging from 0.751-0.940 in brain regions except the right occipital, left anterior cingulate, and left and right cerebellum (ICCs = 0.65-0.741). Although ICCs for inter-rater reliability were fair to excellent in most regions, poor inter-rater correlations were observed for the cingulate and occipital regions. Processing time, including manual skull stripping, was $17.07{\pm}3.43min$. Z-score maps for all participants indicated that cortical thicknesses were not significantly different from those in the comparison databases of healthy adults. Conclusion: BSIM-based cortical thickness measurements provide acceptable intra-rater and inter-rater reliability. We therefore suggest BSIM-based cortical thickness analysis as an adjunct clinical tool to detect cortical atrophy.

• Journal of KIISE:Software and Applications
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• v.36 no.12
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• pp.967-976
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• 2009

In this paper, we propose an automatic lower extremity vessel extraction based on rigid registration and bone elimination techniques in CT and CT angiography images. First, automatic partitioning of the lower extremity based on the anatomy is proposed to consider the local movement of the bone. Second, rigid registration based on distance map is performed to estimate the movement of the bone between CT and CT angiography images. Third, bone elimination and vessel masking techniques are proposed to remove bones in CT angiography image and to prevent the vessel near to bone from eroding. Fourth, post-processing based on vessel tracking is proposed to reduce the effect of misalignment and noises like a cartilage. For the evaluation of our method, we performed the visual inspection, accuracy measures and processing time. For visual inspection, the results of applying general subtraction, registered subtraction and proposed method are compared using volume rendering and maximum intensity projection. For accuracy evaluation, intensity distributions of CT angiography image, subtraction based method and proposed method are analyzed. Experimental result shows that bones are accurately eliminated and vessels are robustly extracted without the loss of other structure. The total processing time of thirteen patient datasets was 40 seconds on average.

• Journal of Korea Multimedia Society
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• v.19 no.4
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• pp.736-746
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• 2016

In this paper, we propose an automatic prostate cancer detection method using position, signal intensity and texture feature based on SVM in multi-parametric MR images. First, to align the prostate on DWI and ADC map to T2wMR, the transformation parameters of DWI are estimated by normalized mutual information-based rigid registration. Then, to normalize the signal intensity range among inter-patient images, histogram stretching is performed. Second, to detect prostate cancer areas in T2wMR, SVM classification with position, signal intensity and texture features was performed on T2wMR, DWI and ADC map. Our feature classification using multi-parametric MR imaging can improve the prostate cancer detection rate on T2wMR.

• Journal of the Korea Computer Graphics Society
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• v.22 no.4
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• pp.11-19
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• 2016

In this paper, we propose an automatic segmentation method of renal parenchyma on abdominal CT image using graph-cuts with shape constraint based on multi-probabilistic atlas. The proposed method consists of following three steps. First, to use the various shape information of renal parenchyma, multi-probabilistic atlas is generated by cortex-based similarity registration. Second, initial seeds for graph-cuts are extracted by maximum a posteriori (MAP) estimation and renal parenchyma is segmented by graph-cuts with shape constraint. Third, to reduce alignment error of probabilistic atlas and increase segmentation accuracy, registration and segmentation are iteratively performed. To evaluate the performance of proposed method, qualitative and quantitative evaluation are performed. Experimental results show that the proposed method avoids a leakage into neighbor regions with similar intensity of renal parenchyma and shows improved segmentation accuracy.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.4 s.304
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• pp.1-12
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• 2005

Image registration is a process to establish the spatial correspondence between images of the same scene, which are acquired at different view points, at different times, or by different sensors. This paper presents a new algorithm for robust registration of the images acquired by multiple sensors having different modalities; the EO (electro-optic) and IR(infrared) ones in the paper. The two feature-based and intensity-based approaches are usually possible for image registration. In the former selection of accurate common features is crucial for high performance, but features in the EO image are often not the same as those in the R image. Hence, this approach is inadequate to register the E0/IR images. In the latter normalized mutual Information (nHr) has been widely used as a similarity measure due to its high accuracy and robustness, and NMI-based image registration methods assume that statistical correlation between two images should be global. Unfortunately, since we find out that EO and IR images don't often satisfy this assumption, registration accuracy is not high enough to apply to some applications. In this paper, we propose a two-stage NMI-based registration method based on the analysis of statistical correlation between E0/1R images. In the first stage, for robust registration, we propose two preprocessing schemes: extraction of statistically correlated regions (ESCR) and enhancement of statistical correlation by filtering (ESCF). For each image, ESCR automatically extracts the regions that are highly correlated to the corresponding regions in the other image. And ESCF adaptively filters out each image to enhance statistical correlation between them. In the second stage, two output images are registered by using NMI-based algorithm. The proposed method provides prospective results for various E0/1R sensor image pairs in terms of accuracy, robustness, and speed.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.5
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• pp.175-183
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• 2004

In this paper, we propose a new accurate and robust image mosaic technique of the mural underdrawing taken from the infra-red camera, which is based on multiple image registration and adaptive blending technique. The image mosaicing methods which have been developed so far have the following deficits. It is hard to generate a high resolution image when there are regions that do not have features or intensity gradients, and there is a trade-off in overlapping region size in view of registration and blending. We consider these issues as follows. First, in order to mosaic images with neither noticeable features nor intensity gradients, we use a projected supplementary pattern and pseudo color image for features in the image pieces which are registered. Second, we search the overlapping region size with minimum blending error between two adjacent images and then apply blending technique to minimum error overlapping region. Finally, we could find our proposed method is more effective and efficient for image mosaicing than conventional mosaic techniques and also is more adequate for the application of infra-red mural underdrawing mosaicing. Experimental results show the accuracy and robustness of the algorithm

• Journal of Biomedical Engineering Research
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• v.23 no.1
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• pp.39-47
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• 2002

The objective of this study is to provide a Precise form of spatial normalization with affine transformation. The quantitative comparison of the brain architecture across different subjects requires a common coordinate system. For the common coordinate system, not only global brain but also a local region of interest should be spatially normalized. Registration using mutual information generally matches the whose brain well. However. a region of interest may not be normalized compared to the feature-based methods with the landmarks. The hybrid method of this Paper utilizes feature information of the local region as well as intensity similarity. Central gray nuclei of a brain including copus callosum, which is used for feature in Schizophrenia detection, is appropriately normalized by the hybrid method. In the results section. our method is compared with mutual information only method and Talairach mapping with schizophrenia Patients. and is shown how it accurately normalizes feature .