scholarly journals Adaptive Diffeomorphic Multiresolution Demons and Their Application to Same Modality Medical Image Registration with Large Deformation

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Chang Wang ◽  
Qiongqiong Ren ◽  
Xin Qin ◽  
Yi Yu
Keyword(s):  
Image Registration ◽  
Large Deformation ◽  
Medical Image ◽  
Medical Images ◽  
Nonrigid Registration ◽  
Synthetic Image ◽  
Medical Image Registration ◽  
Evaluation Indexes ◽  
Quantitative Analyses ◽  
Registration Result

Diffeomorphic demons can guarantee smooth and reversible deformation and avoid unreasonable deformation. However, the number of iterations needs to be set manually, and this greatly influences the registration result. In order to solve this problem, we proposed adaptive diffeomorphic multiresolution demons in this paper. We used an optimized framework with nonrigid registration and diffeomorphism strategy, designed a similarity energy function based on grey value, and stopped iterations adaptively. This method was tested by synthetic image and same modality medical image. Large deformation was simulated by rotational distortion and extrusion transform, medical image registration with large deformation was performed, and quantitative analyses were conducted using the registration evaluation indexes, and the influence of different driving forces and parameters on the registration result was analyzed. The registration results of same modality medical images were compared with those obtained using active demons, additive demons, and diffeomorphic demons. Quantitative analyses showed that the proposed method’s normalized cross-correlation coefficient and structural similarity were the highest and mean square error was the lowest. Medical image registration with large deformation could be performed successfully; evaluation indexes remained stable with an increase in deformation strength. The proposed method is effective and robust, and it can be applied to nonrigid registration of same modality medical images with large deformation.

scholarly journals Multimodal Medical Image Registration Based on an Information-Theory Measure with Histogram Estimation of Continuous Image Representation

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Bicao Li ◽  
Guanyu Yang ◽  
Zhoufeng Liu ◽  
Jean Louis Coatrieux ◽  
Huazhong Shu
Keyword(s):  
Image Registration ◽  
Medical Image ◽  
Medical Images ◽  
Image Representation ◽  
Superior Performance ◽  
Geometric Transformation ◽  
Medical Image Registration ◽  
Continuous Image ◽  
Joint Histogram ◽  
Histogram Estimation

This work presents a novel method for multimodal medical registration based on histogram estimation of continuous image representation. The proposed method, regarded as “fast continuous histogram estimation,” employs continuous image representation to estimate the joint histogram of two images to be registered. The Jensen–Arimoto (JA) divergence is a similarity measure to measure the statistical dependence between medical images from different modalities. The estimated joint histogram is exploited to calculate the JA divergence in multimodal medical image registration. In addition, to reduce the grid effect caused by the grid-aligning transformations between two images and improve the implementation speed of the registration method, random samples instead of all pixels are extracted from the images to be registered. The goal of the registration is to optimize the JA divergence, which would be maximal when two misregistered images are perfectly aligned using the downhill simplex method, and thus to get the optimal geometric transformation. Experiments are conducted on an affine registration of 2D and 3D medical images. Results demonstrate the superior performance of the proposed method compared to standard histogram, Parzen window estimations, particle filter, and histogram estimation based on continuous image representation without random sampling.

Medical Image Registration in Clinical Diagnosis

2019 ◽  
pp. 263-281
Author(s):  
A. Swarnambiga ◽  
Vasuki S.
Keyword(s):  
Image Registration ◽  
Clinical Diagnosis ◽  
Medical Image ◽  
Medical Images ◽  
Medical Image Registration ◽  
B Spline ◽  
Conventional Methods

The term medical image covers a wide variety of types of images (modality), especially in medical image registration with very different perspective. In this chapter, spatial technique is approached and analyzed for providing effective clinical diagnosis. The effective conventional methods are chosen for this registration. Researchers have developed and focused this research using proven conventional methods in the respective fields of registration Affine, Demons, and Affine with B-spline. From the overall analysis, it is clear that Affine with B-Spline performs better in registration of medical images than Affine and Demons.

scholarly journals Algorithm Selection in Multimodal Medical Image Registration

2020 ◽  
Vol 14 (2) ◽  
pp. 10
Author(s):  
Husein Elkeshreu ◽  
Otman Basir
Keyword(s):  
Image Registration ◽  
Medical Image ◽  
Medical Images ◽  
Medical Model ◽  
Medical Image Registration ◽  
Algorithm Selection ◽  
Registration System ◽  
Surgical Guidance ◽  
Registration Algorithm ◽  
Novel Approach

Over the past few decades, fast<strong>-</strong>growth has occurred in the area of medical image acquisition devices, and physicians now rely on the utilization of medical images for the diagnosis, treatment plans, and surgical guidance. Researchers have classified medical images according to two structures: anatomical and functional structures. Due to this classification, the data obtained from two or more images of the same object frequently provide complementary and more abundant information through a process known as multimodal medical model registration. Image registration is spatially mapping the coordinate system of the two images obtained from a different viewpoint and utilizing various sensors. Several automatic multimodal medical image registration algorithms have been introduced based on types of medical images and their applications to increase the reliability, robustness, and accuracy. Due to the diversity in imaging and the different demands<strong> </strong>for applications, there is no single registration algorithm that can do that. This paper introduces a novel method for developing a multimodal medical image registration system that can select the most accepted registration algorithm from a group of registration algorithms for a variety of input datasets. The method described here is based on a machine learning technique that selects the most promising candidate. Several experiments have been conducted, and the results reveal that the novel approach leads to considerably faster reliability, accuracy, and more robustness registration algorithm selection.

An Improved Approach of Mutual Information Based Registration for Medical Images

2013 ◽  
Vol 433-435 ◽  
pp. 368-371
Author(s):  
Shun Sen Guo ◽  
Yong Xia ◽  
Kuan Quan Wang
Keyword(s):  
Image Registration ◽  
Mutual Information ◽  
Similarity Measure ◽  
Medical Image ◽  
Communication Theory ◽  
Medical Images ◽  
Image Data ◽  
Reference Image ◽  
Medical Image Registration ◽  
The Way

Mutual information stems from communication theory, which is commonly used as similarity measure in the field of medical image registration. This approach works directly with image data; no pre-processing or segmentation is required. But calculating the mutual information of images needs a large amount of computation, which in some respect restricts its application. In this paper, by doing some processing on the reference image before the registration, we changed the way of calculating the mutual information to reduce the computation. The result of the experiments shows that the accuracy of registration does not change significantly, whereas the time of calculating the mutual information is decreased significantly.

scholarly journals Comparison of Salient Point Detection Methods for 3D Medical Images

2005 ◽  
Author(s):  
Bryn Lloyd ◽  
Gabor Szekely ◽  
Ron Kikinis ◽  
Simon Warfield
Keyword(s):  
Image Registration ◽  
Medical Image ◽  
Medical Images ◽  
Displacement Measurement ◽  
Detection Methods ◽  
Medical Image Registration ◽  
Salient Point ◽  
Salient Points ◽  
Point Detection

Salient points are used for various applications, such as medical image registration, tracking, stereoscopic matching. The purpose of this paper is to compare two commonly used methods to extract salient points in 3D medical images. We give an interpretation of the methods and validate their performance empirically based on criteria derived for the task of image registration, displacement measurement and tracking in medical images.

scholarly journals A Medical Image Registration Method Based on Progressive Images

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Qian Zheng ◽  
Qiang Wang ◽  
Xiaojuan Ba ◽  
Shan Liu ◽  
Jiaofen Nan ◽  
...  
Keyword(s):  
Image Registration ◽  
Mutual Information ◽  
Medical Image ◽  
Medical Image Analysis ◽  
Reference Image ◽  
Medical Image Registration ◽  
Registration Method ◽  
Multimodal Registration ◽  
Coarse Registration ◽  
Registration Result

Background. Medical image registration is an essential task for medical image analysis in various applications. In this work, we develop a coarse-to-fine medical image registration method based on progressive images and SURF algorithm (PI-SURF) for higher registration accuracy. Methods. As a first step, the reference image and the floating image are fused to generate multiple progressive images. Thereafter, the floating image and progressive image are registered to get the coarse registration result based on the SURF algorithm. For further improvement, the coarse registration result and the reference image are registered to perform fine image registration. The appropriate progressive image has been investigated by experiments. The mutual information (MI), normal mutual information (NMI), normalized correlation coefficient (NCC), and mean square difference (MSD) similarity metrics are used to demonstrate the potential of the PI-SURF method. Results. For the unimodal and multimodal registration, the PI-SURF method achieves the best results compared with the mutual information method, Demons method, Demons+B-spline method, and SURF method. The MI, NMI, and NCC of PI-SURF are improved by 15.5%, 1.31%, and 7.3%, respectively, while MSD decreased by 13.2% for the multimodal registration compared with the optimal result of the state-of-the-art methods. Conclusions. The extensive experiments show that the proposed PI-SURF method achieves higher quality of registration.

RESEARCH ON ROBUSTNESS OF THE MUTUAL INFORMATION SIMILARITY METRIC FOR REGISTRATION OF MEDICAL IMAGES

2011 ◽  
Vol 23 (06) ◽  
pp. 479-491 ◽  
Author(s):  
Mei-Sen Pan ◽  
Fen Zhang ◽  
Qiu-Sheng Rong ◽  
Hui-Can Zhou ◽  
Fang-Yan Nie
Keyword(s):  
Image Registration ◽  
Mutual Information ◽  
Medical Image ◽  
Medical Images ◽  
Image Data ◽  
Medical Image Registration ◽  
Quadratic Spline ◽  
Similarity Metric ◽  
B Spline ◽  
Interpolation Methods

For the past few years, the medical image registration technology has made rapid and significant progress, and has been extensively applied for the 2D/3D medical image processing. However, the robustness of the similarity metric in the medical image registration is rarely studied. In this paper, the mutual information-based registration technology is introduced and the concept of the robustness of the similarity metric is defined. The robustness of the mutual information similarity metric is analyzed and discussed from three aspects such as interpolation methods, image data loss and noise corruption after the linear, quadratic spline, cubic spline, quadratic B-spline, and cubic B-spline interpolations are elaborated and studied. The robustness experiments reveal that the mutual information similarity metric can obtain good robustness in the case of the use of various interpolation methods in the medical image registration; the mutual information similarity metric can also keep good robustness in the case of a slight loss of image data. However, the metric will fail to register the images on the condition that the medical images are seriously incomplete. In addition, we find, if the medical images corrupted by the salt & pepper noise, then the metric basically can succeed in aligning the medical images; but regretfully, the metric fully fail under the condition of the medical images corrupted by the Gaussian noise.

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