Medical Image Registration Using B-Spline Transform

2016 ◽  
Author(s):  
Guest Editor Jianping Du
Keyword(s):  
Image Registration ◽  
Medical Image ◽  
Medical Image Registration ◽  
B Spline

scholarly journals Accelerating B-spline interpolation on GPUs: Application to medical image registration

2020 ◽  
Vol 193 ◽  
pp. 105431 ◽  
Author(s):  
Orestis Zachariadis ◽  
Andrea Teatini ◽  
Nitin Satpute ◽  
Juan Gómez-Luna ◽  
Onur Mutlu ◽  
...  
Keyword(s):  
Image Registration ◽  
Medical Image ◽  
Spline Interpolation ◽  
Medical Image Registration ◽  
B Spline

Unsupervised end-to-end Brain Tumor Magnetic Resonance Image Registration using RBCNN: Rigid Transformation, B-Spline Transformation and Convolutional Neural Network

2021 ◽  
Vol 17 ◽  
Author(s):  
Senthil Pandi Sankareswaran ◽  
Mahadevan Krishnan
Keyword(s):  
Neural Network ◽  
Brain Tumor ◽  
Magnetic Resonance ◽  
Image Registration ◽  
Convolutional Neural Network ◽  
Medical Image ◽  
Computer Assisted ◽  
Rigid Transformation ◽  
Medical Image Registration ◽  
B Spline

Background: Image registration is the process of aligning two or more images in a single coordinate. Now a days, medical image registration plays a significant role in computer assisted disease diagnosis, treatment, and surgery. The different modalities available in the medical image makes medical image registration as an essential step in Computer Assisted Diagnosis(CAD), Computer-Aided Therapy (CAT) and Computer-Assisted Surgery (CAS). Problem definition: Recently many learning based methods were employed for disease detection and classification but those methods were not suitable for real time due to delayed response and need of pre alignment,labeling. Method: The proposed research constructed a deep learning model with Rigid transform and B-Spline transform for medical image registration for an automatic brain tumour finding. The proposed research consists of two steps. First steps uses Rigid transformation based Convolutional Neural Network and the second step uses B-Spline transform based Convolutional Neural Network. The model is trained and tested with 3624 MR (Magnetic Resonance) images to assess the performance. The researchers believe that MR images helps in success the treatment of brain tumour people. Result: The result of the proposed method is compared with the Rigid Convolutional Neural Network (CNN), Rigid CNN + Thin-Plat Spline (TPS), Affine CNN, Voxel morph, ADMIR (Affine and Deformable Medical Image Registration) and ANT(Advanced Normalization Tools) using DICE score, average symmetric surface distance (ASD), and Hausdorff distance. Conclusion: The RBCNN model will help the physician to automatically detect and classify the brain tumor quickly(18 Sec) and efficiently with out doing any pre-alignment and labeling.

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.

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.

Computing the Medical Image Registration Using Meta-Heuristics

2014 ◽  
Vol 643 ◽  
pp. 237-242 ◽  
Author(s):  
Tahari Abdou El Karim ◽  
Bendakmousse Abdeslam ◽  
Ait Aoudia Samy
Keyword(s):  
Image Processing ◽  
Genetic Algorithms ◽  
Simulated Annealing ◽  
Image Registration ◽  
Cost Function ◽  
Population Genetic ◽  
Medical Image ◽  
Medical Image Registration ◽  
Anatomical Structures

The image registration is a very important task in image processing. In the field of medical imaging, it is used to compare the anatomical structures of two or more images taken at different time to track for example the evolution of a disease. Intensity-based techniques are widely used in the multi-modal registration. To have the best registration, a cost function expressing the similarity between these images is maximized. The registration problem is reduced to the optimization of a cost function. We propose to use neighborhood meta-heuristics (tabu search, simulated annealing) and a meta-heuristic population (genetic algorithms). An evaluation step is necessary to estimate the quality of registration obtained. In this paper we present some results of medical image registration

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