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.

Image Registration Method Based on PCA-SIFT Feature Detection

2013 ◽  
Vol 712-715 ◽  
pp. 2395-2398
Author(s):  
Yan Wei Wang ◽  
Hui Li Yu
Keyword(s):  
Image Registration ◽  
Feature Detection ◽  
Scale Invariant ◽  
Registration Method ◽  
Sift Algorithm ◽  
Sift Feature ◽  
Principle Components Analysis ◽  
Key Points ◽  
Components Analysis ◽  
Analysis Scale

SIFT (scale invariant key points) which can well handle images with varying orientation and zoom, is widely used in image registration, but the algorithm is complexity and the processing time is too long. Therefore we used the PCA-SIFT (Principle Components Analysis-scale invariant key points) in image registration. Compared the SIFT descriptor, the PCA-SIFT reduced the dimensions of SIFT feature, enhanced the matching accuracy and reduce the elapsed time. Then the mutual information method used in this paper to estimate the best points. Experimental results show that PCA-SIFT algorithm is simplified, robust and liable.

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.

scholarly journals SIFT Image Feature Extraction for an Efficient Image Registration

2020 ◽  
Vol 8 (6) ◽  
pp. 4090-4094
Keyword(s):  
Image Registration ◽  
Image Feature ◽  
Scale Invariant ◽  
Invariant Feature ◽  
Image Registration Algorithm ◽  
Key Points ◽  
Feature Transform ◽  
Scale Invariant Feature ◽  
Ransac Algorithm ◽  
Unit Vectors

This paper presents an image registration algorithm based on SIFT (Scale Invariant Feature Transform).The obtained descriptors and key points by the SIFT confirms that, the algorithm is very robust to scaling, noise, translation and rotation. At the beginning, the key points are extracted from the image. Later to Match the obtained points, dot products between the unit vectors are calculated. Finally, transformation matrix is obtained by applying RANSAC algorithm. Experimental results shows that the algorithm extracts the better key points, which can be used for used for image registration applications.

scholarly journals Automated Mosaicking of Medical x-ray Images using SIFT Algorithm

2021 ◽  
Vol 23 (05) ◽  
pp. 686-693
Author(s):  
Sanjeevakumar Harihar ◽  
◽  
Dr. Manjunath R ◽  
Keyword(s):  
Image Registration ◽  
Feature Detection ◽  
Similarity Measures ◽  
Body Part ◽  
The Body ◽  
Scale Invariant ◽  
X Ray ◽  
Detection Techniques ◽  
Single Frame ◽  
Panoramic Images

Image registration is a process of joining any number of images that have similar overlapping regions of the same scene in order to make a panoramic image. In the field of medical, multimedia, and image processing applications image registration process stands challenging. The work presented here on medical images can be applicable for long limb operations and scoliosis operations. Traditional x-ray machines produce a single frame of x-ray image containing a portion of the body part, but they can not generate a large view of body x-ray image in a single frame. This problem can be solved by creating panoramic images by combining multiple images. The work proposed in this paper can automatically produce panoramic x-ray images by stitching multiple x-ray images. The proposed work uses scale-invariant feature transform (SIFT) for mosaicking x-ray images as a local feature point extractor which uses the difference of Gaussian (DOG) and invariant to orientation and scale. Based on the location relationship of x-ray images, random sample consensus (RANSAC) is incorporated to remove the effect of mismatched point pairs in x-ray images and to generate the panoramic view. The performance of the system is computed by using structural and time constraint parameters and is compared with different feature detection techniques. The experimental results show that combing SIFT and RANSAC yields less processing time with an increase in similarity measures.

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