scholarly journals A Comparative Study of Automatic Localization Algorithms for Spherical Markers within 3D MRI Data

2021 ◽  
Vol 11 (7) ◽  
pp. 876
Author(s):  
Christian Fiedler ◽  
Paul-Philipp Jacobs ◽  
Marcel Müller ◽  
Silke Kolbig ◽  
Ronny Grunert ◽  
...  
Keyword(s):  
Image Processing ◽  
Human Interaction ◽  
Connected Components ◽  
3D Mri ◽  
Blob Detection ◽  
Localization Algorithms ◽  
Related Approach ◽  
Marker Design ◽  
Bone Anchors ◽  
Detection And Localization

Localization of features and structures in images is an important task in medical image-processing. Characteristic structures and features are used in diagnostics and surgery planning for spatial adjustments of the volumetric data, including image registration or localization of bone-anchors and fiducials. Since this task is highly recurrent, a fast, reliable and automated approach without human interaction and parameter adjustment is of high interest. In this paper we propose and compare four image processing pipelines, including algorithms for automatic detection and localization of spherical features within 3D MRI data. We developed a convolution based method as well as algorithms based on connected-components labeling and analysis and the circular Hough-transform. A blob detection related approach, analyzing the Hessian determinant, was examined. Furthermore, we introduce a novel spherical MRI-marker design. In combination with the proposed algorithms and pipelines, this allows the detection and spatial localization, including the direction, of fiducials and bone-anchors.

scholarly journals Neural silences can be localized rapidly using noninvasive scalp EEG

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Alireza Chamanzar ◽  
Marlene Behrmann ◽  
Pulkit Grover
Keyword(s):  
Center Of Mass ◽  
Cost Effective ◽  
Human Head ◽  
Head Model ◽  
Cortical Function ◽  
Scalp Eeg ◽  
Localization Algorithms ◽  
Important Benefit ◽  
Eeg Recordings ◽  
Detection And Localization

AbstractA rapid and cost-effective noninvasive tool to detect and characterize neural silences can be of important benefit in diagnosing and treating many disorders. We propose an algorithm, SilenceMap, for uncovering the absence of electrophysiological signals, or neural silences, using noninvasive scalp electroencephalography (EEG) signals. By accounting for the contributions of different sources to the power of the recorded signals, and using a hemispheric baseline approach and a convex spectral clustering framework, SilenceMap permits rapid detection and localization of regions of silence in the brain using a relatively small amount of EEG data. SilenceMap substantially outperformed existing source localization algorithms in estimating the center-of-mass of the silence for three pediatric cortical resection patients, using fewer than 3 minutes of EEG recordings (13, 2, and 11mm vs. 25, 62, and 53 mm), as well for 100 different simulated regions of silence based on a real human head model (12 ± 0.7 mm vs. 54 ± 2.2 mm). SilenceMap paves the way towards accessible early diagnosis and continuous monitoring of altered physiological properties of human cortical function.

Rapid and non-invasive surface crack detection for pressed-panel products based on online image processing

2019 ◽  
Vol 18 (5-6) ◽  
pp. 1928-1942 ◽  
Author(s):  
Hwee Kwon Jung ◽  
Gyuhae Park
Keyword(s):  
Image Processing ◽  
Manufacturing Process ◽  
Crack Detection ◽  
Experimental Results ◽  
Inspection System ◽  
Camera System ◽  
Non Invasive ◽  
Panel Products ◽  
Traditional Approaches ◽  
Detection And Localization

Crack detection during the manufacturing process of pressed-panel products is an important aspect of quality management. Traditional approaches for crack detection of those products are subjective and expensive because they are usually performed by experienced human inspectors. Therefore, the development and implementation of an automated and accurate inspection system is required for the manufacturing process. In this article, a crack detection technique based on image processing is proposed that utilizes the images of panel products captured by a regular camera system. First, the binary panel object image is extracted from various backgrounds after considering the color factor. Edge lines are then generated from a binary image using a percolation process. Finally, crack detection and localization is performed with a unique edge-line evaluation. In order to demonstrate the capability of the proposed technique, lab-scale experiments were carried out with a thin aluminum plate. In addition, a test was performed with the panel images acquired at an actual press line. Experimental results show that the proposed technique could effectively detect panel cracks at an improved rate and speed. The experimental results also demonstrate that the proposed technique could be an extension of structural health monitoring frameworks into a new manufacturing application.

scholarly journals An algorithm to measure unsymmetrical circle shape of intravascular ultrasound image using image processing techniques

2021 ◽  
Vol 10 (1) ◽  
pp. 508-515
Author(s):  
Suhaili Beeran Kutty ◽  
Rahmita Wirza O. K. Rahmat ◽  
Sazzli Shahlan Kassim ◽  
Hizmawati Madzin ◽  
Hazlina Hamdan
Keyword(s):  
Image Processing ◽  
Intravascular Ultrasound ◽  
Ultrasound Image ◽  
Region Of Interest ◽  
Processing Technique ◽  
Human Interaction ◽  
Image Processing Technique ◽  
Border Detection ◽  
Cross Sectional ◽  
Minimum Diameter

In diagnosing coronary artery disease, measurement of the cross-sectional area of the lumen, maximum and minimum diameter is very important. Mainly, it will be used to confirm the diagnosing, to predict the stenosis if any and to ensure the size of the stent to be used. However, the measurement only offers by the existing software and some of the software needs human interaction to complete the process. The purpose of this paper is to present the algorithm to measure the region of interest (ROI) on intravascular ultrasound (IVUS) using an image processing technique. The methodology starts with image acquisition process followed by image segmentation. After that, border detection for each ROI was detected and the algorithm was applied to calculate the corresponding region. The result shows that the measurement is accurate and could be used not only for IVUS but applicable to solid circle and unsymmetrical circle shape. 

scholarly journals Instance Segmentation on Real time Object Detection using Mask R-CNN

2019 ◽  
Vol 9 (1) ◽  
pp. 2932-2935
Keyword(s):  
Image Processing ◽  
Search Algorithm ◽  
Human Interaction ◽  
Unique Object ◽  
Detection Of Objects ◽  
Morphological Transformations ◽  
Processing Techniques ◽  
Instance Segmentation ◽  
Image Mask ◽  
Real World Problems

In the ever-advancing field of computer vision, image processing plays a prominent role. We can extend the applications of Image processing into solving real-world problems like substantially decreasing Human interaction over the art of driving. In the process of achieving this task, we face several challenges like Segmentation and Detection of objects. The proposed thesis overcomes the challenges effectively by introducing Instance segmentation and Binary masks along with Keras and Tensorflow. Instance segmentation is used to delineate and detect every unique object of interest according to their pixel characteristics in an image. Mask RCNN is the superior model over the existing CNN models and yields accurate detection of objects more efficiently. Unlike conventional Neural Networks which employs selective search algorithm to identify object of interest, Mask RCNN employs Regional Proposal Networks(RPN) to identify object of interest. For better results Image pre-processing techniques and morphological transformations are employed to reduce the noise and increase pixel clarity

FPGA-Based Object Detection and Motion Tracking in Micro- and Nanorobotics

2014 ◽  
pp. 251-261
Author(s):  
Claas Diederichs ◽  
Sergej Fatikow
Keyword(s):  
Image Processing ◽  
Object Detection ◽  
High Speed ◽  
Motion Tracking ◽  
Principal Component ◽  
Fpga Implementation ◽  
Connected Components ◽  
Connected Component ◽  
Connected Component Labeling ◽  
Labeling Approach

Object-detection and classification is a key task in micro- and nanohandling. The microscopic imaging is often the only available sensing technique to detect information about the positions and orientations of objects. FPGA-based image processing is superior to state of the art PC-based image processing in terms of achievable update rate, latency and jitter. A connected component labeling algorithm is presented and analyzed for its high speed object detection and classification feasibility. The features of connected components are discussed and analyzed for their feasibility with a single-pass connected component labeling approach, focused on principal component analysis-based features. It is shown that an FPGA implementation of the algorithm can be used for high-speed tool tracking as well as object classification inside optical microscopes. Furthermore, it is shown that an FPGA implementation of the algorithm can be used to detect and classify carbon-nanotubes (CNTs) during image acquisition in a scanning electron microscope, allowing fast object detection before the whole image is captured.

Application of Camera Image Processing to Control of Humanoid Robot Motion

2014 ◽  
Vol 474 ◽  
pp. 179-185
Author(s):  
Rastislav Ďuriš
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Humanoid Robot ◽  
Humanoid Robots ◽  
Robot Motion ◽  
Potential Applications ◽  
Processing Techniques ◽  
And Control ◽  
Detection And Localization ◽  
Wide Potential

The wide potential applications of humanoid robots require that the robots can move in general environment, overcome various obstacles, detect predefined objects and control of its motion according to all these parameters. The goal of this paper is address the problem of implementation of computer vision to motion control of humanoid robot. We focus on using of computer vision and image processing techniques, based on which the robot can detect and recognize a predefined color object in a captured image. An algorithm to detection and localization of objects is described. The results obtained from image processing are used in an algorithm for controlling of the robot movement.

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