Object Segmentation Based on a Nonparametric Snake with Motion Prediction in Video

2012 ◽  
pp. 86-107
Author(s):  
Sang-Myoung Ye ◽  
Rae-Hong Park ◽  
Dong-Kyu Lee
Keyword(s):  
Object Segmentation ◽  
Computation Time ◽  
Absolute Difference ◽  
Motion Prediction ◽  
Surveillance Systems ◽  
Gradient Vector ◽  
Snake Model ◽  
Object Contour ◽  
The Mean ◽  
Initial Object

Object segmentation in video sequence is a basic and important task in video applications such as surveillance systems and video coding. Nonparametric snake algorithms for object segmentation have been proposed to overcome the drawback of conventional snake algorithms: the dependency on several parameters. In this chapter, a new object segmentation algorithm for video, based on a nonparametric snake model with motion prediction, is proposed. Object contour is initialized by using the mean absolute difference of intensity between input and previous frames. And in order to convert initial object contours into more exact object contours, the gradient vector flow snake is used. Finally object contour is predicted using a Kalman filter in successive frames. The proposed object segmentation method for video can provide more detailed and improved object segmentation results than the conventional methods. Various experimental results show the effectiveness of the proposed method in terms of the pixel-based quality measure and the computation time.

Segmentasi Citra Mri Menggunakan Deteksi Tepi Untuk Identifikasi Kanker Payudara

2017 ◽  
Vol 15 (2) ◽  
pp. 17 ◽  
Author(s):  
Ervina Varijki ◽  
Bambang Krismono Triwijoyo
Keyword(s):  
Breast Cancer ◽  
Edge Detection ◽  
Object Segmentation ◽  
Computation Time ◽  
Input Image ◽  
Boundary Line ◽  
Cancer Breast ◽  
Average Computation Time ◽  
Breast Cancer Mri ◽  
Image Format

One type of cancer that is capable identified using MRI technology is breast cancer. Breast cancer is still the leading cause of death world. therefore early detection of this disease is needed. In identifying breast cancer, a doctor or radiologist analyzing the results of magnetic resonance image that is stored in the format of the Digital Imaging Communication In Medicine (DICOM). It takes skill and experience sufficient for diagnosis is appropriate, andaccurate, so it is necessary to create a digital image processing applications by utilizing the process of object segmentation and edge detection to assist the physician or radiologist in identifying breast cancer. MRI image segmentation using edge detection to identification of breast cancer using a method stages gryascale change the image format, then the binary image thresholding and edge detection process using the latest Robert operator. Of the20 tested the input image to produce images with the appearance of the boundary line of each region or object that is visible and there are no edges are cut off, with the average computation time less than one minute.

Usefulness of point-of-care ultrasound in military medical emergencies performed by young military medicine residents

2019 ◽  
pp. jramc-2018-001132
Author(s):  
Pierre Perrier ◽  
J Leyral ◽  
O Thabouillot ◽  
D Papeix ◽  
G Comat ◽  
...  
Keyword(s):  
Diagnostic Accuracy ◽  
Clinical Examination ◽  
Point Of Care ◽  
Military Medicine ◽  
Final Diagnosis ◽  
Absolute Difference ◽  
Point Of Care Ultrasound ◽  
Medical Emergencies ◽  
The Mean ◽  
A Prospective Study

IntroductionTo evaluate the usefulness of point-of-care ultrasound (POCUS) performed by young military medicine residents after short training in the diagnosis of medical emergencies.MethodsA prospective study was performed in the emergency department of a French army teaching hospital. Two young military medicine residents received ultrasound training focused on gall bladder, kidneys and lower limb veins. After clinical examination, they assigned a ‘clinicaldiagnostic probability’ (CP) on a visual analogue scale from 0 (definitely not diagnosis) to 10 (definitive diagnosis). The same student performed ultrasound examination and assigned an ‘ultrasounddiagnostic probability’ (UP) in the same way. The absolute difference between CP and UP was calculated. This result corresponded to the Ultrasound Diagnostic Index (UDI), which was positive if UP was closer to the final diagnosis than CP (POCUS improved the diagnostic accuracy), and negative conversely (POCUS decreased the diagnostic accuracy).ResultsForty-eight patients were included and 48 ultrasound examinations were performed. The present pathologies were found in 14 patients (29%). The mean UDI value was +3 (0–5). UDI was positive in 35 exams (73%), zero in 12 exams (25%) and negative in only one exam (2%).ConclusionPOCUS performed after clinical examination increases the diagnostic accuracy of young military medicine residents.

scholarly journals Improving peripherally inserted central catheter appropriateness and reducing device-related complications: a quasiexperimental study in 52 Michigan hospitals

2021 ◽  
pp. bmjqs-2021-013015
Author(s):  
Vineet Chopra ◽  
Megan O'Malley ◽  
Jennifer Horowitz ◽  
Qisu Zhang ◽  
Elizabeth McLaughlin ◽  
...  
Keyword(s):  
Central Line ◽  
Hospital Performance ◽  
Policy Implications ◽  
Change Point Analysis ◽  
Absolute Difference ◽  
Peripherally Inserted Central Catheter ◽  
Central Catheter ◽  
Appropriateness Criteria ◽  
Mean Frequency ◽  
The Mean

BackgroundThe Michigan Appropriateness Guide for Intravenous Catheters (MAGIC) provides evidence-based criteria for peripherally inserted central catheter (PICC) use. Whether implementing MAGIC improves PICC appropriateness and reduces complications is unknown.MethodsA quasiexperimental study design to implement MAGIC in 52 Michigan hospitals was used. Data were collected from medical records by trained abstractors. Hospital performance on three appropriateness criteria was measured: short-term PICC use (≤5 days), use of multilumen PICCs and PICC placement in patients with chronic kidney disease. PICC appropriateness and device complications preintervention (January 2013 to December 2016) versus postintervention (January 2017 to January 2020) were compared. Change-point analysis was used to evaluate the effect of the intervention on device appropriateness. Logistic regression and Poisson models were fit to assess the association between appropriateness and complications (composite of catheter occlusion, venous thromboembolism (VTE) and central line-associated bloodstream infection (CLABSI)).ResultsAmong 38 592 PICCs, median catheter dwell ranged from 8 to 56 days. During the preintervention period, the mean frequency of appropriate PICC use was 31.9% and the mean frequency of complications was 14.7%. Following the intervention, PICC appropriateness increased to 49.0% (absolute difference 17.1%, p<0.001) while complications decreased to 10.7% (absolute difference 4.0%, p=0.001). Compared with patients with inappropriate PICC placement, appropriate PICC use was associated with a significantly lower odds of complications (OR 0.29, 95% CI 0.25 to 0.34), including decreases in occlusion (OR 0.25, 95% CI 0.21 to 0.29), CLABSI (OR 0.61, 95% CI 0.46 to 0.81) and VTE (OR 0.40, 95% CI 0.33 to 0.47, all p<0.01). Patients with appropriate PICC placement had lower rate of complications than those with inappropriate PICC use (incidence rate ratio 0.987, 95% CI 0.98 to 0.99, p<0.001).ConclusionsImplementation of MAGIC in Michigan hospitals was associated with improved PICC appropriateness and fewer complications. These findings have important quality, safety and policy implications for hospitals, patients and payors.

scholarly journals A Novel Lithium Foil Cosmic-Ray Neutron Detector for Measuring Field-Scale Soil Moisture

2021 ◽  
Vol 3 ◽  
Author(s):  
Andres Patrignani ◽  
Tyson E. Ochsner ◽  
Benjamin Montag ◽  
Steven Bellinger
Keyword(s):  
Soil Moisture ◽  
Water Content ◽  
Atmospheric Pressure ◽  
Lower Cost ◽  
Cosmic Ray ◽  
Absolute Difference ◽  
Atmospheric Conditions ◽  
Volumetric Soil Water Content ◽  
The Mean

During the past decade, cosmic-ray neutron sensing technology has enabled researchers to reveal soil moisture spatial patterns and to estimate landscape-average soil moisture for hydrological and agricultural applications. However, reliance on rare materials such as helium-3 increases the cost of cosmic-ray neutron probes (CRNPs) and limits the adoption of this unique technology beyond the realm of academic research. In this study, we evaluated a novel lower cost CRNP based on moderated ultra-thin lithium-6 foil (Li foil system) technology against a commercially-available CRNP based on BF3 (boron trifluoride, BF-3 system). The study was conducted in a cropped field located in the Konza Prairie Biological Station near Manhattan, Kansas, USA (325 m a.s.l.) from 10 April 2020 to 18 June 2020. During this period the mean atmospheric pressure was 977 kPa, the mean air relative humidity was 70%, and the average volumetric soil water content was 0.277 m3 m−3. Raw fast neutron counts were corrected for atmospheric pressure, atmospheric water vapor, and incoming neutron flux. Calibration of the CRNPs was conducted using four intensive field surveys (n &gt; 120), in combination with continuous observations from an existing array of in situ soil moisture sensors. The time series of uncorrected neutron counts of the Li foil system was highly correlated (r2 = 0.91) to that of the BF-3 system. The Li foil system had an average of 2,250 corrected neutron counts per hour with an uncertainty of 2.25%, values that are specific to the instrument size, detector configuration, and atmospheric conditions. The estimated volumetric water content from the Li foil system had a mean absolute difference of 0.022 m3 m−3 compared to the value from the array of in situ sensors. The new Li foil detector offers a promising lower cost alternative to existing cosmic-ray neutron detection devices used for hectometer-scale soil moisture monitoring.

Fast and accurate snake model for object contour detection

2001 ◽  
Vol 37 (10) ◽  
pp. 624 ◽  
Author(s):  
Wen-Nung Lie ◽  
Cheng-Hung Chuang
Keyword(s):  
Contour Detection ◽  
Snake Model ◽  
Object Contour

Object Extraction Using Topological Models from Complex Scene Image

2017 ◽  
pp. 335-357
Author(s):  
Uday Pratap Singh ◽  
Sanjeev Jain
Keyword(s):  
Object Recognition ◽  
Object Segmentation ◽  
Multimedia Data ◽  
Region Merging ◽  
Complex Scene ◽  
Object Contour ◽  
Merging Process ◽  
Feasible Solutions ◽  
Neighbourhood Graph ◽  
Topological Models

Efficient and effective object recognition from a multimedia data are very complex. Automatic object segmentation is usually very hard for natural images; interactive schemes with a few simple markers provide feasible solutions. In this chapter, we propose topological model based region merging. In this work, we will focus on topological models like, Relative Neighbourhood Graph (RNG) and Gabriel graph (GG), etc. From the Initial segmented image, we constructed a neighbourhood graph represented different regions as the node of graph and weight of the edges are the value of dissimilarity measures function for their colour histogram vectors. A method of similarity based region merging mechanism (supervised and unsupervised) is proposed to guide the merging process with the help of markers. The region merging process is adaptive to the image content and it does not need to set the similarity threshold in advance. To the validation of proposed method extensive experiments are performed and the result shows that the proposed method extracts the object contour from the complex background.

scholarly journals miRID: Multi-Modal Image Registration Using Modality-Independent and Rotation-Invariant Descriptor

Symmetry ◽  
2020 ◽  
Vol 12 (12) ◽  
pp. 2078
Author(s):  
Thuvanan Borvornvitchotikarn ◽  
Werasak Kurutach
Keyword(s):  
Image Processing ◽  
Image Registration ◽  
Similarity Measures ◽  
Fundamental Property ◽  
Transformation Process ◽  
Experimental Result ◽  
Absolute Difference ◽  
Rotation Invariant ◽  
The Mean ◽  
Image Registrations

Axiomatically, symmetry is a fundamental property of mathematical functions defining similarity measures, where similarity measures are important tools in many areas of computer science, including machine learning and image processing. In this paper, we investigate a new technique to measure the similarity between two images, a fixed image and a moving image, in multi-modal image registration (MIR). MIR in medical image processing is essential and useful in diagnosis and therapy guidance, but still a very challenging task due to the lack of robustness against the rotational variance in the image transformation process. Our investigation leads to a novel, local self-similarity descriptor, called the modality-independent and rotation-invariant descriptor (miRID). By relying on the mean of the intensity values, an miRID is simply computable and can effectively handle the complicated intensity relationship between multi-modal images. Moreover, it can also overcome the problem of rotational variance by sorting the numerical values, each of which is the absolute difference between each pixel’s intensity and the mean of all pixel intensities within a patch of the image. The experimental result shows that our method outperforms others in both multi-modal rigid and non-rigid image registrations.

scholarly journals Stereo Imaging Using Hardwired Self-Organizing Object Segmentation

Sensors ◽  
2020 ◽  
Vol 20 (20) ◽  
pp. 5833
Author(s):  
Ching-Han Chen ◽  
Guan-Wei Lan ◽  
Ching-Yi Chen ◽  
Yen-Hsiang Huang
Keyword(s):  
Neural Network ◽  
Stereo Vision ◽  
Stereo Matching ◽  
Imaging System ◽  
Object Segmentation ◽  
Hierarchical Architecture ◽  
Absolute Difference ◽  
Stereo Imaging ◽  
Som Neural Network ◽  
Self Organizing

Stereo vision utilizes two cameras to acquire two respective images, and then determines the depth map by calculating the disparity between two images. In general, object segmentation and stereo matching are some of the important technologies that are often used in establishing stereo vision systems. In this study, we implement a highly efficient self-organizing map (SOM) neural network hardware accelerator as unsupervised color segmentation for real-time stereo imaging. The stereo imaging system is established by pipelined, hierarchical architecture, which includes an SOM neural network module, a connected component labeling module, and a sum-of-absolute-difference-based stereo matching module. The experiment is conducted on a hardware resources-constrained embedded system. The performance of stereo imaging system is able to achieve 13.8 frames per second of 640 × 480 resolution color images.

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