Dermoscopic image segmentation method based on convolutional neural networks

In this article the new hybrid iris image segmentation method based on convolutional neural networks and mathematical methods is proposed. Iris boundaries are found using modified Daugman’s method. Two UNet-based convolutional neural networks are used for iris mask detection. The first one is used to predict the preliminary iris mask including the areas of the pupil, eyelids and some eyelashes. The second neural network is applied to the enlarged image to specify thin ends of eyelashes. Then the principal curvatures method is used to combine the predicted by neural networks masks and to detect eyelashes correctly. The pro- posed segmentation algorithm is tested using images from CASIA IrisV4 Interval database. The results of the proposed method are evaluated by the Intersection over Union, Recall and Precision metrics. The average metrics values are 0.922, 0.957 and 0.962, respectively. The proposed hy- brid iris image segmentation approach demonstrates an improvement in comparison with the methods that use only neural networks.

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Accelerating the Training of Convolutional Neural Networks for Image Segmentation with Deep Active Learning

2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) ◽

10.1109/itsc45102.2020.9294260 ◽

2020 ◽

Author(s):

Weitao Chen ◽

Rick Salay ◽

Sean Sedwards ◽

Vahdat Abdelzad ◽

Krzysztof Czarnecki

Keyword(s):

Neural Networks ◽

Image Segmentation ◽

Active Learning ◽

Convolutional Neural Networks

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Hierarchical fusion convolutional neural networks for SAR image segmentation

Pattern Recognition Letters ◽

10.1016/j.patrec.2021.04.005 ◽

2021 ◽

Vol 147 ◽

pp. 115-123

Author(s):

Yinyin Jiang ◽

Ming Li ◽

Peng Zhang ◽

Xiaofeng Tan ◽

Wanying Song

Keyword(s):

Neural Networks ◽

Image Segmentation ◽

Convolutional Neural Networks ◽

Sar Image ◽

Sar Image Segmentation

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Information Fusion in Deep Convolutional Neural Networks for Biomedical Image Segmentation 1

Signal Processing and Machine Learning for Biomedical Big Data ◽

10.1201/9781351061223-15 ◽

2018 ◽

pp. 301-312

Author(s):

Mohammad Havaei ◽

Nicolas Guizard ◽

Nicolas Chapados ◽

Yoshua Bengio

Keyword(s):

Neural Networks ◽

Image Segmentation ◽

Convolutional Neural Networks ◽

Information Fusion ◽

Deep Convolutional Neural Networks ◽

Biomedical Image

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A System for Accurate Tracking and Video Recordings of Rodent Eye Movements using Convolutional Neural Networks for Biomedical Image Segmentation

2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) ◽

10.1109/embc.2018.8513072 ◽

2018 ◽

Cited By ~ 1

Author(s):

Isha Puri ◽

David D. Cox

Keyword(s):

Neural Networks ◽

Image Segmentation ◽

Eye Movements ◽

Convolutional Neural Networks ◽

Biomedical Image ◽

Video Recordings

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Ensemble of Deep Convolutional Neural Networks with Monte Carlo Dropout Sampling for Automated Image Segmentation Quality Control and Robust Deep Learning Using Small Datasets

Medical Image Understanding and Analysis - Lecture Notes in Computer Science ◽

10.1007/978-3-030-80432-9_22 ◽

2021 ◽

pp. 280-293

Author(s):

Evan Hann ◽

Ricardo A. Gonzales ◽

Iulia A. Popescu ◽

Qiang Zhang ◽

Vanessa M. Ferreira ◽

...

Keyword(s):

Neural Networks ◽

Quality Control ◽

Monte Carlo ◽

Image Segmentation ◽

Deep Learning ◽

Convolutional Neural Networks ◽

Deep Convolutional Neural Networks ◽

Segmentation Quality

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Semantic Image Segmentation with Deep Convolutional Neural Networks and Quick Shift

Symmetry ◽

10.3390/sym12030427 ◽

2020 ◽

Vol 12 (3) ◽

pp. 427 ◽

Cited By ~ 1

Author(s):

Sanxing Zhang ◽

Zhenhuan Ma ◽

Gang Zhang ◽

Tao Lei ◽

Rui Zhang ◽

...

Keyword(s):

Neural Networks ◽

Image Segmentation ◽

Convolutional Neural Networks ◽

Semantic Segmentation ◽

Autonomous Driving ◽

Input Image ◽

Feature Representation ◽

Segmentation Algorithm ◽

Deep Convolutional Neural Networks ◽

Semantic Image Segmentation

Semantic image segmentation, as one of the most popular tasks in computer vision, has been widely used in autonomous driving, robotics and other fields. Currently, deep convolutional neural networks (DCNNs) are driving major advances in semantic segmentation due to their powerful feature representation. However, DCNNs extract high-level feature representations by strided convolution, which makes it impossible to segment foreground objects precisely, especially when locating object boundaries. This paper presents a novel semantic segmentation algorithm with DeepLab v3+ and super-pixel segmentation algorithm-quick shift. DeepLab v3+ is employed to generate a class-indexed score map for the input image. Quick shift is applied to segment the input image into superpixels. Outputs of them are then fed into a class voting module to refine the semantic segmentation results. Extensive experiments on proposed semantic image segmentation are performed over PASCAL VOC 2012 dataset, and results that the proposed method can provide a more efficient solution.

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