scholarly journals An Edge Detection Method for Suspicious Local Regions in CT images with Jaxtapleural Nodules

2018 ◽  
Vol 232 ◽  
pp. 02056 ◽  
Author(s):  
Changli Feng ◽  
Haiyan Wei ◽  
Min Li ◽  
Xin Li ◽  
Min Ding
Keyword(s):  
Detection Algorithm ◽  
Lung Nodules ◽  
Lung Region ◽  
Ct Image ◽  
Feature Points ◽  
Point Matching ◽  
Sift Algorithm ◽  
Intensity Information ◽  
Great Performance ◽  
Edge Detection Method

Juxtapleural lung nodules are often excluded from the lung region in many CT image processing algorithms which are based on intensity information. For solving this problem, a suspicious edge line detection algorithm is proposed to obtain the edge line of the suspicious local lung region in this manuscript. Firstly, the lung region in the CT image is extracted by a fixed threshold. Then a SIFT algorithm is used to detect the feature point in the lung region. To filter out the useless feature points, a closest point matching method is used. Then a K-mean method is introduced to divide those feature points into several parts in which the edges of juxtapleural Lung nodules are contained. Experiments over CT slices show that the proposed method has a great performance in detecting the edge line of suspicious regions.

Adaptive Edge Detection Method Towards Features Extraction From Diverse Medical Imaging Technologies

2018 ◽  
pp. 1245-1278
Author(s):  
Indra Kanta Maitra ◽  
Samir Kumar Bandhyopadhyaay
Keyword(s):  
Edge Detection ◽  
Medical Image ◽  
Medical Image Analysis ◽  
Detection Algorithm ◽  
Edge Detection Algorithm ◽  
Detection Techniques ◽  
Robust Segmentation ◽  
Image Characteristics ◽  
Edge Detection Method ◽  
Medical Image Enhancement

The CAD is a relatively young interdisciplinary technology, has had a tremendous impact on medical diagnosis specifically cancer detection. The accuracy of CAD to detect abnormalities on medical image analysis requires a robust segmentation algorithm. To achieve accurate segmentation, an efficient edge-detection algorithm is essential. Medical images like USG, X-Ray, CT and MRI exhibit diverse image characteristics but are essentially collection of intensity variations from which specific abnormalities are needed to be isolated. In this chapter a robust medical image enhancement and edge detection algorithm is proposed, using tree-based adaptive thresholding technique. It has been compared with different classical edge-detection techniques using one sample two tail t-test to exam whether the null hypothesis can be supported. The proposed edge-detection algorithm showing 0.07 p-values and 2.411 t-stat where α = 0.025. Moreover the proposed edge is single pixeled and connected which is very significant for medical edge detection.

Polynomial Interpolation Sub-Pixel Edge Detection Method Based on Improved Canny Operator

2014 ◽  
Vol 563 ◽  
pp. 203-207
Author(s):  
Kun Lin Yu ◽  
Zhi Yu Xie
Keyword(s):  
Edge Detection ◽  
Detection Method ◽  
Polynomial Interpolation ◽  
Detection Algorithm ◽  
Edge Image ◽  
Canny Operator ◽  
Edge Detection Algorithm ◽  
Canny Edge ◽  
Image Edge ◽  
Edge Detection Method

According to the shortcoming of traditional Canny edge detection algorithm is sensitive to noise and low positioning accuracy, this paper proposes an algorithm of Polynomial interpolation Sub-pixel edge detection based on improved Canny operator: We first use improved Canny operator edge detection algorithm to extract rough image edge, then use the quadratic Polynomial interpolation to calculate on the rough extraction edge, finally refine the edge image. Experiments show that the improved method is better than the traditional detection method can accurately locate the image edge.

Measurements in the Wake of a Ventilated Hydrofoil

2013 ◽  
Author(s):  
Seung-Jae Lee ◽  
Ellison Kawakami ◽  
Roger E. A. Arndt
Keyword(s):  
Bubble Size ◽  
High Speed ◽  
Bubbly Flow ◽  
Detection Algorithm ◽  
Bubble Velocity ◽  
University Of Minnesota ◽  
Bubble Detection ◽  
Global Threshold ◽  
Edge Detection Method ◽  
The University

The purpose of this study is to develop the necessary algorithms to determine the bubble size distribution and velocity in the wake of a ventilated or cavitating hydrofoil utilizing background illumination. A simplified experiment was carried out to validate the automatic bubble detection algorithm at Saint Anthony Falls Laboratory (SAFL) of the University of Minnesota. The experiment was conducted in the high-speed water tunnel. First, particle shadow velocimetry (PSV) images of a bubbly flow were collected. All parts of the image that are above the global threshold are segmented by an edge detection method based on the Canny algorithm. The utilized algorithm was made to detect partly overlapping bubbles and reconstruct missing parts. After all images have been analyzed, the bubble velocity can be determined by applying a tracking algorithm. This study has shown that the algorithm enables reliable analysis of irregularly shaped bubbles even when bubbles are highly overlapped in the wake of the ventilated hydrofoil. It is expected that this technique can be used to determine the bubble velocity field as well as the bubble size distributions.

scholarly journals Fast Anchor Point Matching for Emergency UAV Image Stitching Using Position and Pose Information

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 2007
Author(s):  
Ruizhe Shao ◽  
Chun Du ◽  
Hao Chen ◽  
Jun Li
Keyword(s):  
Image Stitching ◽  
Feature Points ◽  
Point Matching ◽  
Novel Approach ◽  
Aerial Vehicle ◽  
Speed Up ◽  
Anchor Points ◽  
Uav Images ◽  
Uav Image ◽  
Traditional Approaches

With the development of unmanned aerial vehicle (UAV) techniques, UAV images are becoming more widely used. However, as an essential step of UAV image application, the computation of stitching remains time intensive, especially for emergency applications. Addressing this issue, we propose a novel approach to use the position and pose information of UAV images to speed up the process of image stitching, called FUIS (fast UAV image stitching). This stitches images by feature points. However, unlike traditional approaches, our approach rapidly finds several anchor-matches instead of a lot of feature matches to stitch the image. Firstly, from a large number of feature points, we design a method to select a small number of them that are more helpful for stitching as anchor points. Then, a method is proposed to more quickly and accurately match these anchor points, using position and pose information. Experiments show that our method significantly reduces the time consumption compared with the-state-of-art approaches with accuracy guaranteed.

A New Algorithm of Fast-Generated Panoramic Images

2011 ◽  
Vol 393-395 ◽  
pp. 539-542
Author(s):  
Wei Cong Na
Keyword(s):  
Random Sampling ◽  
Euclidean Distance ◽  
Feature Matching ◽  
Correction Coefficient ◽  
Feature Points ◽  
Generation Efficiency ◽  
Sift Algorithm ◽  
Panoramic Images ◽  
Space Image ◽  
Quality Guarantee

The new algorithm of fast-generated panoramic images this paper puts forward is to extract the feature points of images by the improved SIFT algorithm, and use Euclidean distance combining the K-D tree structure to realize the rapid initial feature matching. Then, based on these initial matching points and the theory of random sampling consistent algorithm, the purification of feature points is realized. At last, the introduction of correction coefficient makes it possible to eliminate fusion ghosts, and HIS space image fusion is applied in order to eliminate the brightness differences. It is verified by the experiments that on the premise of generation of quality guarantee, the new algorithm greatly improves the generation efficiency of panorama images.

Research on Method of Items Recognition Based on SURF Algorithm

2011 ◽  
Vol 121-126 ◽  
pp. 4630-4634
Author(s):  
Wen Yu Chen ◽  
Wen Zhi Xie ◽  
Yan Li Zhao ◽  
Zhong Bo Hao
Keyword(s):  
Euclidean Distance ◽  
Item Recognition ◽  
Image Features ◽  
Feature Points ◽  
Interest Points ◽  
Distance Method ◽  
Sift Algorithm ◽  
Fast Running ◽  
Long Time ◽  
Detection And Recognition

Items detection and recognition have become one of hotspots in the field of computer vision research. Based on image features method has the advantage of low amount of information, fast running speed, high precision, and SIFT algorithm is one of them. But traditional SIFI algorithm have large amount of calculation data and spend long time to compute in terms of items recognition. Therefore, this paper come up with a method of items recognition based on SURF. This article elaborates the basic principle of SURF algorithm that firstly use SURF algorithm to extract feature points of item image, secondly adopt Euclidean distance method to find corresponding interest points of image, and finally get the image after items recognition combination with mapping relation of item image using RANSAC(Random Sample Consesus). Experimental results show that the system of item recognition based on SURF algorithm have better effect on matching recognition, higher instantaneity, better robustness.

Automatic Tracking Tunnel Cables Using Egomotion Estimation

2013 ◽  
Vol 710 ◽  
pp. 546-549
Author(s):  
Chang An Liu ◽  
Zhe Sun ◽  
Hua Wu ◽  
Guo Tian Yang
Keyword(s):  
Detection Algorithm ◽  
Power Line ◽  
Experimental Results ◽  
Feature Points ◽  
Automatic Tracking ◽  
Egomotion Estimation ◽  
The Matrix ◽  
Point Detection

We proposed an online method of tracking the tunnel cable based on egomotion estimation. The method is firstly applied key point detection algorithm to extract feature points, and then the points are matched to estimate the matrix of egomotion representing the camera movement. Finally, we use the matrix to locate a mask around the cable in each frames captured inside the power line tunnel. The experimental results show robustness and efficiency of our method.

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