DISCERN: Generative Framework for Vessel Segmentation using Convolutional Neural Network and Visual Codebook

Chudzik, Piotr; Al-Diri, Bashir; Calivá, Francesco; Hunter, Andrew

doi:10.1109/embc.2018.8513604

Cited by 17 publications

(12 citation statements)

References 16 publications

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“…During this study, we created two separate grasshopper detection models each designed for different purposes: stationary and mobile. To produce these detection frameworks we decided to use deep learning algorithms, which are state-of-the-art machine learning techniques used for various computer vision and image processing tasks, including; detection 25 , segmentation 26 , classification, and localization of objects in pictures and videos 27 . The stationary model was designed to validate our methodological approach and provide baseline performance for the mobile model.…”

Section: Methodsmentioning

confidence: 99%

Mobile Real-Time Grasshopper Detection and Data Aggregation Framework

Chudzik

Mitchell

Alkaseem

et al. 2020

Sci Rep

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insects of the family Orthoptera: Acrididae including grasshoppers and locust devastate crops and ecosystems around the globe. The effective control of these insects requires large numbers of trained extension agents who try to spot concentrations of the insects on the ground so that they can be destroyed before they take flight. This is a challenging and difficult task. No automatic detection system is yet available to increase scouting productivity, data scale and fidelity. Here we demonstrate MAESTRO, a novel grasshopper detection framework that deploys deep learning within RBG images to detect insects. MAeStRo uses a state-of-the-art two-stage training deep learning approach. the framework can be deployed not only on desktop computers but also on edge devices without internet connection such as smartphones. MAeStRo can gather data using cloud storage for further research and in-depth analysis. In addition, we provide a challenging new open dataset (GHCID) of highly variable grasshopper populations imaged in inner Mongolia. the detection performance of the stationary method and the mobile App are 78 and 49 percent respectively; the stationary method requires around 1000 ms to analyze a single image, whereas the mobile app uses only around 400 ms per image. The algorithms are purely data-driven and can be used for other detection tasks in agriculture (e.g. plant disease detection) and beyond. This system can play a crucial role in the collection and analysis of data to enable more effective control of this critical global pest.

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Section: Methodsmentioning

confidence: 99%

Mobile Real-Time Grasshopper Detection and Data Aggregation Framework

Chudzik

Mitchell

Alkaseem

et al. 2020

Sci Rep

Self Cite

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“…and PCNN (Pulse-coupled neural network) [116]. CNN uses information from shallow to deep layers to determine the fine details and overall structure of retinal vessels [101][102][103][104]133]. An example of CNN network design might be an input convolution layer containing 1x28x28 patches.…”

Section: H Supervised Learning Algorithms and Deep Learningmentioning

confidence: 99%

“…Due to the consistency of the tables, the Table XIII below does not contain these parameters AUC in articles Soomro et al [101], Chudzik et al [103], Hajabdollahi et al [102], Guo et al [104], Sengür et al [110], Feng et al [112], Soomro et al [114] and AU ROC parameter in articles Mo et al [108], Lahiri et al [109], Wang et al [132], Wu et al [125] and PPV in article Feng et al [112] and visual comparison in article Gu et al [133]. It contains only Acc, Se and Sp.…”

Section: H Supervised Learning Algorithms and Deep Learningmentioning

confidence: 99%

“…Table XVI shows the evaluation of algorithms for retinal blood vessel segmentation based on the objective AUC parameter. The table shows that based on the AUC parameter, the best methods for blood vessel segmentation are those based on supervised learning, which involves deep neural network learning [119], convolutional neural networks [103,104,108,112,120,130] and classification based on decision and random trees [107,141].…”

Section: Evaluation Of Segmentation Algorithms Based On Objective mentioning

confidence: 99%

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Recent Techniques and Trends for Retinal Blood Vessel Extraction and Tortuosity Evaluation: A Comprehensive Review

2020

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“…It also employs a novel global arterio-venous ratio (AVR) measure to detect significant changes in diabetic retinopathy (DR) cases. Chudzik et al [10] proposed blood vessels segmentation approach and reported the performance of their method with the DRIVE and STARE databases. They achieved AUC of 0.964 on the DRIVE database and an AUC of 0.983 on the STARE database.…”

Section: Literature Reviewmentioning

confidence: 99%

Mathematical Morphology based Retinal Image Blood Vessels Segmentation

2019

IJITEE

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It is necessary to verify the state of blood vessel network in the retina for diagnosing various issues associated with eyes. In this research paper, an involuntary retinal vessel segmentation using mathematical morphology is proposed. The contrast of the retinal images is enhanced by contrast limited adaptive histogram equalisation technique. Ten blood vessels of the enhanced retinal image are detected using morphological processing. The hysteresis thresholding is applied on the blood vessels detected image to remove the unwanted back ground detail. Finally the properly segmented binary image of the retinal vessel is obtained using post processing process. Results of the presented method are verified by using most widely used for benchmarking retinal image databases such as, Child Heart and Health Study in England (CHASE_DB1) and Digital Retinal Images for Vessel Extraction (DRIVE) database by computing the evaluation metrics such as sensitivity, specificity, accuracy and precision. The better evaluation metrics achieved for the DRIVE dataset are 0.7493, 0.9687, 0.9524 and 0.6590, and the worst values are 0.6621, 0.9411, 0.9137 and 0.5491. The best evaluation metrics values for the CHASE_DB1 dataset are 0.5058, 0.8947, 0.9382 and 0.8856, and the worst values are 0.5639, 0.9581, 0.9137 and 0.7110. The investigational results show that the suggested approach provides the excellent accuracy in comparison with other approaches

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DISCERN: Generative Framework for Vessel Segmentation using Convolutional Neural Network and Visual Codebook

Cited by 17 publications

References 16 publications

Mobile Real-Time Grasshopper Detection and Data Aggregation Framework

Mobile Real-Time Grasshopper Detection and Data Aggregation Framework

Recent Techniques and Trends for Retinal Blood Vessel Extraction and Tortuosity Evaluation: A Comprehensive Review

Mathematical Morphology based Retinal Image Blood Vessels Segmentation

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