Centromere detection of human metaphase chromosome images using a candidate based method

Accurate detection of the human metaphase chromosome centromere is a critical element of cytogenetic diagnostic techniques, including chromosome enumeration, karyotyping and radiation biodosimetry. Existing centromere detection methods tends to perform poorly in the presence of irregular boundaries, shape variations and premature sister chromatid separation. We present a centromere detection algorithm that uses a novel contour partitioning technique to generate centromere candidates followed by a machine learning approach to select the best candidate that enhances the detection accuracy. The contour partitioning technique evaluates various combinations of salient points along the chromosome boundary using a novel feature set and is able to identify telomere regions as well as detect and correct for sister chromatid separation. This partitioning is used to generate a set of centromere candidates which are then evaluated based on a second set of proposed features. The proposed algorithm outperforms previously published algorithms and is shown to do so with a larger set of chromosome images. A highlight of the proposed algorithm is the ability to rank this set of centromere candidates and create a centromere confidence metric which may be used in post-detection analysis. When tested with a larger metaphase chromosome database consisting of 1400 chromosomes collected from 40 metaphase cell images, the proposed algorithm was able to accurately localize 1220 centromere locations yielding a detection accuracy of 87%.

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Centromere Detection of Human Metaphase Chromosome Images using a Candidate Based Method

10.1101/032110 ◽

2015 ◽

Cited By ~ 2

Author(s):

Akila Subasinghe ◽

Jagath Samarabandu ◽

Yanxin Li ◽

Ruth Wilkins ◽

Farrah Flegal ◽

...

Keyword(s):

Sister Chromatid ◽

Metaphase Chromosome ◽

Detection Algorithm ◽

Diagnostic Techniques ◽

Thickness Measurement ◽

Critical Element ◽

Detection Accuracy ◽

Sister Chromatid Separation ◽

Confidence Value ◽

Radiation Biodosimetry

AbstractAccurate detection of the human metaphase chromosome centromere is an critical element of cytogenetic diagnostic techniques, including chromosome enumeration, karyotyping and radiation biodosimetry. Existing image processing methods can perform poorly in the presence of irregular boundaries, shape variations and premature sister chromatid separation, which can adversely affect centromere localization. We present a centromere detection algorithm that uses a novel profile thickness measurement technique on irregular chromosome structures defined by contour partitioning. Our algorithm generates a set of centromere candidates which are then evaluated based on a set of features derived from images of chromosomes. Our method also partitions the chromosome contour to isolate its telomere regions and then detects and corrects for sister chromatid separation. When tested with a chromosome database consisting of 1400 chromosomes collected from 40 metaphase cell images, the candidate based centromere detection algorithm was able to accurately localize 1220 centromere locations yielding a detection accuracy of 87%. We also introduce a Candidate Based Centromere Confidence (CBCC) metric which indicates an approximate confidence value of a given centromere detection and can be readily extended into other candidate related detection problems.

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High-Speed Lightweight Ship Detection Algorithm Based on YOLO-V4 for Three-Channels RGB SAR Image

Remote Sensing ◽

10.3390/rs13101909 ◽

2021 ◽

Vol 13 (10) ◽

pp. 1909

Author(s):

Jiahuan Jiang ◽

Xiongjun Fu ◽

Rui Qin ◽

Xiaoyan Wang ◽

Zhifeng Ma

Keyword(s):

Deep Learning ◽

Gpu Computing ◽

Hot Spot ◽

Detection Algorithm ◽

Detection Methods ◽

Detection Accuracy ◽

Processing Unit ◽

Sar Image ◽

Marine Monitoring ◽

Ship Detection

Synthetic Aperture Radar (SAR) has become one of the important technical means of marine monitoring in the field of remote sensing due to its all-day, all-weather advantage. National territorial waters to achieve ship monitoring is conducive to national maritime law enforcement, implementation of maritime traffic control, and maintenance of national maritime security, so ship detection has been a hot spot and focus of research. After the development from traditional detection methods to deep learning combined methods, most of the research always based on the evolving Graphics Processing Unit (GPU) computing power to propose more complex and computationally intensive strategies, while in the process of transplanting optical image detection ignored the low signal-to-noise ratio, low resolution, single-channel and other characteristics brought by the SAR image imaging principle. Constantly pursuing detection accuracy while ignoring the detection speed and the ultimate application of the algorithm, almost all algorithms rely on powerful clustered desktop GPUs, which cannot be implemented on the frontline of marine monitoring to cope with the changing realities. To address these issues, this paper proposes a multi-channel fusion SAR image processing method that makes full use of image information and the network’s ability to extract features; it is also based on the latest You Only Look Once version 4 (YOLO-V4) deep learning framework for modeling architecture and training models. The YOLO-V4-light network was tailored for real-time and implementation, significantly reducing the model size, detection time, number of computational parameters, and memory consumption, and refining the network for three-channel images to compensate for the loss of accuracy due to light-weighting. The test experiments were completed entirely on a portable computer and achieved an Average Precision (AP) of 90.37% on the SAR Ship Detection Dataset (SSDD), simplifying the model while ensuring a lead over most existing methods. The YOLO-V4-lightship detection algorithm proposed in this paper has great practical application in maritime safety monitoring and emergency rescue.

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Real-Time Vehicle Detection Algorithm Based on Vision and Lidar Point Cloud Fusion

Journal of Sensors ◽

10.1155/2019/8473980 ◽

2019 ◽

Vol 2019 ◽

pp. 1-9 ◽

Cited By ~ 8

Author(s):

Hai Wang ◽

Xinyu Lou ◽

Yingfeng Cai ◽

Yicheng Li ◽

Long Chen

Keyword(s):

Deep Learning ◽

Real Time ◽

Autonomous Vehicles ◽

Point Cloud ◽

Vehicle Detection ◽

Detection Algorithm ◽

Detection Methods ◽

Depth Information ◽

Detection Accuracy ◽

Classification Rate

Vehicle detection is one of the most important environment perception tasks for autonomous vehicles. The traditional vision-based vehicle detection methods are not accurate enough especially for small and occluded targets, while the light detection and ranging- (lidar-) based methods are good in detecting obstacles but they are time-consuming and have a low classification rate for different target types. Focusing on these shortcomings to make the full use of the advantages of the depth information of lidar and the obstacle classification ability of vision, this work proposes a real-time vehicle detection algorithm which fuses vision and lidar point cloud information. Firstly, the obstacles are detected by the grid projection method using the lidar point cloud information. Then, the obstacles are mapped to the image to get several separated regions of interest (ROIs). After that, the ROIs are expanded based on the dynamic threshold and merged to generate the final ROI. Finally, a deep learning method named You Only Look Once (YOLO) is applied on the ROI to detect vehicles. The experimental results on the KITTI dataset demonstrate that the proposed algorithm has high detection accuracy and good real-time performance. Compared with the detection method based only on the YOLO deep learning, the mean average precision (mAP) is increased by 17%.

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Real-time Factory Smoke Detection Based on Two-stage Relation-guided Algorithm

10.21203/rs.3.rs-707862/v1 ◽

2021 ◽

Author(s):

Zhenyu Wang ◽

Senrong Ji ◽

Duokun Yin

Keyword(s):

Real Time ◽

Contextual Information ◽

Search Space ◽

Detection Algorithm ◽

Image Resolution ◽

Single Stage ◽

Detection Methods ◽

Detection Accuracy ◽

Smoke Detection ◽

Two Stage

Abstract Recently, using image sensing devices to analyze air quality has attracted much attention of researchers. To keep real-time factory smoke under universal social supervision, this paper proposes a mobile-platform-running efficient smoke detection algorithm based on image analysis techniques. Since most smoke images in real scenes have challenging variances, it’s difficult for existing object detection methods. To this end, we introduce the two-stage smoke detection (TSSD) algorithm based on the lightweight framework, in which the prior knowledge and contextual information are modeled into the relation-guided module to reduce the smoke search space, which can therefore significantly improve the shortcomings of the single-stage method. Experimental results show that the TSSD algorithm can robustly improve the detection accuracy of the single-stage method and has good compatibility for different image resolution inputs. Compared with various state-of-the-art detection methods, the accuracy AP mean of the TSSD model reaches 59.24%, even surpassing the current detection model Faster R-CNN. In addition, the detection speed of our proposed model can reach 50 ms (20 FPS), which meets the real-time requirements, and can be deployed in the mobile terminal carrier. This model can be widely used in some scenes with smoke detection requirements, providing great potential for practical environmental applications.

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Identifying Communities in Dynamic Networks Using Information Dynamics

Entropy ◽

10.3390/e22040425 ◽

2020 ◽

Vol 22 (4) ◽

pp. 425

Author(s):

Zejun Sun ◽

Jinfang Sheng ◽

Bin Wang ◽

Aman Ullah ◽

FaizaRiaz Khawaja

Keyword(s):

Community Detection ◽

Dynamic Networks ◽

Parameter Tuning ◽

Detection Algorithm ◽

Network Evolution ◽

Detection Methods ◽

Detection Accuracy ◽

Information Dynamics ◽

Dynamic Community Detection ◽

Dynamic Community

Identifying communities in dynamic networks is essential for exploring the latent network structures, understanding network functions, predicting network evolution, and discovering abnormal network events. Many dynamic community detection methods have been proposed from different viewpoints. However, identifying the community structure in dynamic networks is very challenging due to the difficulty of parameter tuning, high time complexity and detection accuracy decreasing as time slices increase. In this paper, we present a dynamic community detection framework based on information dynamics and develop a dynamic community detection algorithm called DCDID (dynamic community detection based on information dynamics), which uses a batch processing technique to incrementally uncover communities in dynamic networks. DCDID employs the information dynamics model to simulate the exchange of information among nodes and aims to improve the efficiency of community detection by filtering out the unchanged subgraph. To illustrate the effectiveness of DCDID, we extensively test it on synthetic and real-world dynamic networks, and the results demonstrate that the DCDID algorithm is superior to the representative methods in relation to the quality of dynamic community detection.

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Machine Committee Framework for Power Grid Disturbances Analysis Using Synchrophasors Data

Smart Cities ◽

10.3390/smartcities4010001 ◽

2020 ◽

Vol 4 (1) ◽

pp. 1-16

Author(s):

Haoran Niu ◽

Olufemi A. Omitaomu ◽

Qing C. Cao

Keyword(s):

Event Detection ◽

Change Point ◽

Power Grid ◽

Detection Algorithm ◽

Change Point Detection ◽

Detection Methods ◽

Detection Accuracy ◽

Disturbance Analysis ◽

Events Detection ◽

Point Detection

Events detection is a key challenge in power grid frequency disturbances analysis. Accurate detection of events is crucial for situational awareness of the power system. In this paper, we study the problem of events detection in power grid frequency disturbance analysis using synchrophasors data streams. Current events detection approaches for power grid rely on individual detection algorithm. This study integrates some of the existing detection algorithms using the concept of machine committee to develop improved detection approaches for grid disturbance analysis. Specifically, we propose two algorithms—an Event Detection Machine Committee (EDMC) algorithm and a Change-Point Detection Machine Committee (CPDMC) algorithm. Both algorithms use parallel architecture to fuse detection knowledge of its individual methods to arrive at an overall output. The EDMC algorithm combines five individual event detection methods, while the CPDMC algorithm combines two change-point detection methods. Each method performs the detection task separately. The overall output of each algorithm is then computed using a voting strategy. The proposed algorithms are evaluated using three case studies of actual power grid disturbances. Compared with the individual results of the various detection methods, we found that the EDMC algorithm is a better fit for analyzing synchrophasors data; it improves the detection accuracy; and it is suitable for practical scenarios.

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Application of Bidimensional Empirical Mode Decomposition to Medical Liquid Opacity Detection

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.128-129.530 ◽

2011 ◽

Vol 128-129 ◽

pp. 530-533

Author(s):

Jian Wan ◽

Yuan Peng Diao ◽

Dong Mei Yan ◽

Qiang Guo ◽

Zhen Shen Qu

Keyword(s):

Edge Detection ◽

Empirical Mode Decomposition ◽

External Environment ◽

Detection Algorithm ◽

Detection Methods ◽

Detection Accuracy ◽

Edge Detection Algorithm ◽

Mode Decomposition ◽

Simulation Results ◽

Bidimensional Empirical Mode Decomposition

A Robert operator edge detection algorithm based on Bidimensional Empirical Mode Decomposition (BEMD) to detect medical liquid opacity is proposed. This method can effectively resolve the problem that traditional Robert operator edge detection can be easily effected by noise, and it also has certain effects on restraining external environment influence. The simulation results show that, compare with traditional medical liquid opacity detection methods, the proposed method could achieve higher detection accuracy, and has a certain theory and application value.

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A Novel Active Anomaly Discovery Method and its Applications in Additive Manufacturing

10.36227/techrxiv.16674412.v1 ◽

2021 ◽

Author(s):

Bo Shen ◽

Zhenyu Kong

Keyword(s):

Additive Manufacturing ◽

Anomaly Detection ◽

State Of The Art ◽

Detection Algorithm ◽

The State ◽

Detection Accuracy ◽

Detection Algorithms ◽

Detection Analysis ◽

Unsupervised Anomaly Detection ◽

Discovery Method

Anomaly detection aims to identify the true anomalies from a given set of data instances. Unsupervised anomaly detection algorithms are applied to an unlabeled dataset by producing a ranked list based on anomaly scores. Unfortunately, due to the inherent limitations, many of the top-ranked instances by unsupervised algorithms are not anomalies or not interesting from an application perspective, which leads to high false-positive rates. Active anomaly discovery (AAD) is proposed to overcome this deficiency, which sequentially selects instances to get the labeling information and incorporate it into the anomaly detection algorithm to improve the detection accuracy iteratively. However, labeling is often costly. Therefore, the way to balance detection accuracy and labeling cost is essential. Along this line, this paper proposes a novel AAD method to achieve the goal. Our approach is based on the state-of-the-art unsupervised anomaly detection algorithm, namely, Isolation Forest, to extract features. Thereafter, the sparsity of the extracted features is utilized to improve its anomaly detection performance. To enforce the sparsity of the features and subsequent improvement of the detection analysis, a new algorithm based on online gradient descent, namely, Sparse Approximated Linear Anomaly Discovery (SALAD), is proposed with its theoretical Regret analysis. Extensive experiments on both open-source and additive manufacturing datasets demonstrate that the proposed algorithm significantly outperforms the state-of-the-art algorithms for anomaly detection.

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Solder Joint Defect Detection in the Connectors Using Improved Faster-RCNN Algorithm

Applied Sciences ◽

10.3390/app11020576 ◽

2021 ◽

Vol 11 (2) ◽

pp. 576

Author(s):

Kaihua Zhang ◽

Haikuo Shen

Keyword(s):

Solder Joint ◽

Defect Detection ◽

Data Augmentation ◽

Detection Method ◽

Detection Algorithm ◽

Detection Methods ◽

Detection Accuracy ◽

Data Set ◽

Electronic Products ◽

Original Algorithm

The miniaturization and high integration of electronic products have higher and higher requirements for welding of internal components of electronic products. A welding quality detection method has always been one of the important research contents in the industry, among which, the research on solder joint defect detection of a connector has gradually attracted people’s attention with the development of image detection algorithm. The traditional solder joint detection method of connector adopts manual detection or automatic detection methods, which is inefficient and not safe enough. With the development of deep learning, the application of a deep convolutional neural network to target detection has become a research hotspot. In this paper, a data set of connector solder joint samples was made and the number of image samples was expanded to more than 3 times of the original by using data augmentation. Clustering generates anchor boxes and transfer learning with ResNet-101 were fused, so an improved faster region-based convolutional neural networks (Faster RCNN) algorithm was proposed. The experiment verified that the improved algorithm proposed in this paper had a great improvement in all aspects compared with the original algorithm. The average detection accuracy of this method can reach 94%, and the detection rate of some defects can even reach 100%, which can completely meet the industrial requirements.

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Scene distortion detection algorithm using multitemporal remote sensing images

Computer Optics ◽

10.18287/2412-6179-2019-43-5-869-885 ◽

2019 ◽

Vol 43 (5) ◽

pp. 869-885 ◽

Cited By ~ 1

Author(s):

A.M. Belov ◽

A.Y. Denisova

Keyword(s):

Remote Sensing ◽

Detection Algorithm ◽

Detection Methods ◽

Natural Phenomenon ◽

Detection Accuracy ◽

Remote Sensing Images ◽

Image Capture ◽

Binary Images ◽

Multitemporal Remote Sensing ◽

Opaque Object

Multitemporal remote sensing images of a particular territory might include accidental scene distortions. Scene distortion is a significant local brightness change caused by the scene overlap with some opaque object or a natural phenomenon coincident with the moment of image capture, for example, clouds and shadows. The fact that different images of the scene are obtained at different instants of time makes the appearance, location and shape of scene distortions accidental. In this article we propose an algorithm for detecting accidental scene distortions using a dataset of multitemporal remote sensing images. The algorithm applies superpixel segmentation and anomaly detection methods to get binary images of scene distortion location for each image in the dataset. The algorithm is adapted to handle images with different spectral and spatial sampling parameters, which makes it more multipurpose than the existing solutions. The algorithm's quality was assessed using model images with scene distortions for two remote sensing systems. The experiments showed that the proposed algorithm with the optimal settings can reach a detection accuracy of about 90% and a false detection error of about 10%.

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