InvCos Curvature Patch Image Registration Technique for Accurate Segmentation of Autistic Brain Images

2021 ◽  
pp. 659-666
Author(s):  
N. Nagashree ◽  
Premjyoti Patil ◽  
Shantakumar Patil ◽  
Mallikarjun Kokatanur
Keyword(s):  
Image Registration ◽  
Brain Images ◽  
Patch Image

scholarly journals ADMIR–Affine and Deformable Medical Image Registration for Drug-Addicted Brain Images

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 70960-70968 ◽  
Author(s):  
Kun Tang ◽  
Zhi Li ◽  
Lili Tian ◽  
Lihui Wang ◽  
Yuemin Zhu
Keyword(s):  
Image Registration ◽  
Medical Image ◽  
Medical Image Registration ◽  
Brain Images

Automatic volumetric registration of NCCT and CTA brain images using intensity based image registration

2014 ◽  
Author(s):  
Israna H. Arka ◽  
Kalaivani Chellappan ◽  
Shahizon A. Mukari ◽  
Zhe K. Law ◽  
Ramesh Sahathevan ◽  
...  
Keyword(s):  
Image Registration ◽  
Brain Images ◽  
Volumetric Registration

scholarly journals Mjolnir: Extending HAMMER Using a Diffusion Transformation Model and Histogram Equalization for Deformable Image Registration

2009 ◽  
Vol 2009 ◽  
pp. 1-18 ◽  
Author(s):  
Lotta M. Ellingsen ◽  
Jerry L. Prince
Keyword(s):  
Image Registration ◽  
Medical Image Analysis ◽  
Computation Time ◽  
Deformable Image Registration ◽  
Histogram Equalization ◽  
Image Features ◽  
Transformation Model ◽  
Brain Images ◽  
Novel Approach ◽  
Feature Correspondences

Image registration is a crucial step in many medical image analysis procedures such as image fusion, surgical planning, segmentation and labeling, and shape comparison in population or longitudinal studies. A new approach to volumetric intersubject deformable image registration is presented. The method, called Mjolnir, is an extension of the highly successful method HAMMER. New image features in order to better localize points of correspondence between the two images are introduced as well as a novel approach to generate a dense displacement field based upon the weighted diffusion of automatically derived feature correspondences. An extensive validation of the algorithm was performed on T1-weighted SPGR MR brain images from the NIREP evaluation database. The results were compared with results generated by HAMMER and are shown to yield significant improvements in cortical alignment as well as reduced computation time.

scholarly journals Structural Representation: Reducing Multi-Modal Image Registration to Mono-Modal Problem

2015 ◽  
Vol 1 (1) ◽  
Author(s):  
Keyvan Kasiri ◽  
David Clausi ◽  
Paul Fieguth
Keyword(s):  
Image Registration ◽  
Mutual Information ◽  
The Other ◽  
Optimization Process ◽  
Computational Time ◽  
Phase Congruency ◽  
Structural Representation ◽  
Brain Images ◽  
Modal Approach ◽  
Gradient Information

<p>Registration of multi-modal images has been a challenging task<br />due to the complex intensity relationship between images. The<br />standard multi-modal approach tends to use sophisticated similarity<br />measures, such as mutual information, to assess the accuracy<br />of the alignment. Employing such measures imply the increase in<br />the computational time and complexity, and makes it highly difficult<br />for the optimization process to converge. The presented registration<br />method works based on structural representations of images<br />captured from different modalities, in order to convert the multimodal<br />problem into a mono-modal one. Two different representation<br />methods are presented. One is based on a combination of<br />phase congruency and gradient information of the input images,<br />and the other utilizes a modified version of entropy images in a<br />patch-based manner. Sample results are illustrated based on experiments<br />performed on brain images from different modalities.</p>

scholarly journals T1 and T2 MRI Brain Images Registration and Fusion Technique

2020 ◽  
Vol 8 (6) ◽  
pp. 1113-1117
Keyword(s):  
Image Registration ◽  
Averaging Method ◽  
Brain Pathology ◽  
Target Image ◽  
Brain Images ◽  
Mri Brain ◽  
Multimodal Images ◽  
Mri Brain Images ◽  
T2 Mri ◽  
Pathology Image

Fusion of the medical images and registering them will improve the diagnosis and treatment for brain pathology. Image registration plays a major role because multimodal images intensity levels are to be aligned based on relationship between the images. Image registration is proposed where T1 image is a target image where T2 is registering image. Optical flow with SIFT is applied to register T2 image. The registered T2 image is fused with T1 image by applying curvelet transformation and averaging method. Entropy and Mutual Information (MI) parameter is used to evaluate the system performance. The results of the system give better entropy and MI value.

Image Registration in Ultrasound-Assisted Brain Surgery

2018 ◽  
pp. 123-145
Author(s):  
Haradhan Chel ◽  
Prabin Kumar Bora
Keyword(s):  
Image Registration ◽  
Similarity Measures ◽  
Optimization Method ◽  
Brain Surgery ◽  
Deformation Model ◽  
Brain Images ◽  
Diseased Person ◽  
Preoperative Magnetic Resonance ◽  
Mr Brain Images ◽  
The Brain

Image registration is an essential step in the image guided brain surgery. A preoperative magnetic resonance (MR) image guides the neurosurgeon about the size and the location of the tumor inside the brain of the diseased person. Due to several reasons, brain shift occurs during the surgery, results in the shift of the actual position of the tumor. Intra-operative MR imaging is expensive and may not be financially viable for many hospitals. An effective intraoperative US can be used in replacement of MR. For performing registration of US and MR images, the most of the state-of-the-art methods use a suitable similarity or dissimilarity measure, a spline based deformation model, a smoothing technique and an effective fast optimization method. This chapter starts with a discussion on various types of brain tumors and their clinical significance. It also covers on various similarity measures, optimizations and the available database of US and MR brain images.

scholarly journals Multiresolution image registration for multimodal brain images and fusion for better neurosurgical planning

2017 ◽  
Vol 40 (6) ◽  
pp. 329-338 ◽  
Author(s):  
Siddeshappa Nandish ◽  
Gopalakrishna Prabhu ◽  
Kadavigere V. Rajagopal
Keyword(s):  
Image Registration ◽  
Brain Images ◽  
Neurosurgical Planning ◽  
Multiresolution Image

scholarly journals Multi-Modal Medical Image Registration with Full or Partial Data: A Manifold Learning Approach

2018 ◽  
Vol 5 (1) ◽  
pp. 5 ◽  
Author(s):  
Fereshteh Bashiri ◽  
Ahmadreza Baghaie ◽  
Reihaneh Rostami ◽  
Zeyun Yu ◽  
Roshan D’Souza
Keyword(s):  
Image Registration ◽  
Medical Image ◽  
Absolute Error ◽  
Transformation Method ◽  
Full Data ◽  
Brain Images ◽  
Partial Data ◽  
Multiple Imaging ◽  
Structural Differences ◽  
Partial Overlap

Multi-modal image registration is the primary step in integrating information stored in two or more images, which are captured using multiple imaging modalities. In addition to intensity variations and structural differences between images, they may have partial or full overlap, which adds an extra hurdle to the success of registration process. In this contribution, we propose a multi-modal to mono-modal transformation method that facilitates direct application of well-founded mono-modal registration methods in order to obtain accurate alignment of multi-modal images in both cases, with complete (full) and incomplete (partial) overlap. The proposed transformation facilitates recovering strong scales, rotations, and translations. We explain the method thoroughly and discuss the choice of parameters. For evaluation purposes, the effectiveness of the proposed method is examined and compared with widely used information theory-based techniques using simulated and clinical human brain images with full data. Using RIRE dataset, mean absolute error of 1.37, 1.00, and 1.41 mm are obtained for registering CT images with PD-, T1-, and T2-MRIs, respectively. In the end, we empirically investigate the efficacy of the proposed transformation in registering multi-modal partially overlapped images.

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