Nonrigid image registration method for thoracic CT images using vessel structure information

2012 ◽  
Author(s):  
Shinya Maeda ◽  
Hyoungseop Kim ◽  
Joo Kooi Tan ◽  
Seiji Ishikawa ◽  
Seiichi Murakami ◽  
...  
Keyword(s):  
Image Registration ◽  
Ct Images ◽  
Registration Method ◽  
Nonrigid Image Registration ◽  
Structure Information ◽  
Thoracic Ct ◽  
Vessel Structure

scholarly journals Nonrigid Registration of Lung CT Images Based on Tissue Features

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Rui Zhang ◽  
Wu Zhou ◽  
Yanjie Li ◽  
Shaode Yu ◽  
Yaoqin Xie
Keyword(s):  
Image Registration ◽  
Medical Image ◽  
Feature Detection ◽  
Ct Images ◽  
Image Process ◽  
Scale Invariant ◽  
Nonrigid Image Registration ◽  
Breathing Motion ◽  
Key Points ◽  
High Gradients

Nonrigid image registration is a prerequisite for various medical image process and analysis applications. Much effort has been devoted to thoracic image registration due to breathing motion. Recently, scale-invariant feature transform (SIFT) has been used in medical image registration and obtained promising results. However, SIFT is apt to detect blob features. Blobs key points are generally detected in smooth areas which may contain few diagnostic points. In general, diagnostic points used in medical image are often vessel crossing points, vascular endpoints, and tissue boundary points, which provide abundant information about vessels and can reflect the motion of lungs accurately. These points generally have high gradients as opposed to blob key points and can be detected by Harris. In this work, we proposed a hybrid feature detection method which can detect tissue features of lungs effectively based on Harris and SIFT. In addition, a novel method which can remove mismatched landmarks is also proposed. A series of thoracic CT images are tested by using the proposed algorithm, and the quantitative and qualitative evaluations show that our method is statistically significantly better than conventional SIFT method especially in the case of large deformation of lungs during respiration.

A new parametric nonrigid image registration method based on Helmholtz's theorem

2008 ◽  
Author(s):  
Hsi-Yue Hsiao ◽  
Hua-mei Chen ◽  
Ting-Hung Lin ◽  
Chih-Yao Hsieh ◽  
Mei-Yi Chu ◽  
...  
Keyword(s):  
Image Registration ◽  
Registration Method ◽  
Nonrigid Image Registration

SU-E-J-86: A Deformable Image Registration Method for Dose Accumulation Between IMRT and HDR CT Images

2013 ◽  
Vol 40 (6Part8) ◽  
pp. 170-170
Author(s):  
X Zhen ◽  
H Yan ◽  
J Hu ◽  
L Zhou ◽  
X Jia ◽  
...  
Keyword(s):  
Image Registration ◽  
Deformable Image Registration ◽  
Ct Images ◽  
Registration Method ◽  
Dose Accumulation

An Image Registration Method Combing Feature Constraint with Multilevel Strategy

2011 ◽  
Vol 58-60 ◽  
pp. 286-291
Author(s):  
Hong Kui Xu ◽  
Ming Yan Jiang ◽  
Ming Qiang Yang
Keyword(s):  
Image Registration ◽  
Optimization Methods ◽  
Local Extremum ◽  
Parametric Image ◽  
Registration Accuracy ◽  
Registration Method ◽  
Structure Information ◽  
Local Difference ◽  
Intelligent Optimization Algorithms ◽  
Image Registrations

A novel method combing feature constraint with multilevel strategy to improve simultaneously the registration accuracy and speed is proposed for non-parametric image registrations. To images between which the local difference is large, integrating feature constraint constructed with local structure information of images into objective function of image registration improves the registration accuracy. When applying feature constraint under multilevel strategy, parameter searching is prevented from entrapped into local extremum by using the optimization result on coarser levels as the starting points on finer levels; meanwhile traditional optimization methods without demanding intelligent optimization algorithms which consume more time can find the accurate registration parameter on finer levels, so registration speed is improved. Experimental results indicate that this method can finish fast and accurate registration for images between which there exists large local difference.

A Deformable Image Registration Method for Dose Accumulation between HDR CT Images

Brachytherapy ◽  
2014 ◽  
Vol 13 ◽  
pp. S15-S16 ◽  
Author(s):  
Xin Zhen ◽  
Haibin Chen ◽  
Hao Yan ◽  
Linghong Zhou ◽  
Loren Mell ◽  
...  
Keyword(s):  
Image Registration ◽  
Deformable Image Registration ◽  
Ct Images ◽  
Registration Method ◽  
Dose Accumulation

scholarly journals Temporal subtraction CT with nonrigid image registration improves detection of bone metastases by radiologists: results of a large-scale observer study

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Koji Onoue ◽  
Masahiro Yakami ◽  
Mizuho Nishio ◽  
Ryo Sakamoto ◽  
Gakuto Aoyama ◽  
...  
Keyword(s):  
Image Registration ◽  
Bone Metastases ◽  
Large Scale ◽  
Ct Images ◽  
Operating Characteristics ◽  
Nonrigid Image Registration ◽  
Temporal Subtraction ◽  
Significant Difference ◽  
Image Pairs ◽  
Mean Sensitivity

AbstractTo determine whether temporal subtraction (TS) CT obtained with non-rigid image registration improves detection of various bone metastases during serial clinical follow-up examinations by numerous radiologists. Six board-certified radiologists retrospectively scrutinized CT images for patients with history of malignancy sequentially. These radiologists selected 50 positive and 50 negative subjects with and without bone metastases, respectively. Furthermore, for each subject, they selected a pair of previous and current CT images satisfying predefined criteria by consensus. Previous images were non-rigidly transformed to match current images and subtracted from current images to automatically generate TS images. Subsequently, 18 radiologists independently interpreted the 100 CT image pairs to identify bone metastases, both without and with TS images, with each interpretation separated from the other by an interval of at least 30 days. Jackknife free-response receiver operating characteristics (JAFROC) analysis was conducted to assess observer performance. Compared with interpretation without TS images, interpretation with TS images was associated with a significantly higher mean figure of merit (0.710 vs. 0.658; JAFROC analysis, P = 0.0027). Mean sensitivity at lesion-based was significantly higher for interpretation with TS compared with that without TS (46.1% vs. 33.9%; P = 0.003). Mean false positive count per subject was also significantly higher for interpretation with TS than for that without TS (0.28 vs. 0.15; P < 0.001). At the subject-based, mean sensitivity was significantly higher for interpretation with TS images than that without TS images (73.2% vs. 65.4%; P = 0.003). There was no significant difference in mean specificity (0.93 vs. 0.95; P = 0.083). TS significantly improved overall performance in the detection of various bone metastases.

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