Feature extraction, image segmentation, and scene reconstruction

1997 ◽  
Author(s):  
Eric D. Lester ◽  
Ross T. Whitaker ◽  
Mongi A. Abidi
Keyword(s):  
Feature Extraction ◽  
Image Segmentation ◽  
Scene Reconstruction

A Densely Connected Network Based on U-Net for Medical Image Segmentation

2021 ◽  
Vol 17 (3) ◽  
pp. 1-14
Author(s):  
Zhenzhen Yang ◽  
Pengfei Xu ◽  
Yongpeng Yang ◽  
Bing-Kun Bao
Keyword(s):  
Feature Extraction ◽  
Image Segmentation ◽  
Loss Function ◽  
Network Architecture ◽  
Medical Image ◽  
Medical Image Segmentation ◽  
Cross Entropy ◽  
Loss Functions ◽  
Feature Maps ◽  
Different Levels

The U-Net has become the most popular structure in medical image segmentation in recent years. Although its performance for medical image segmentation is outstanding, a large number of experiments demonstrate that the classical U-Net network architecture seems to be insufficient when the size of segmentation targets changes and the imbalance happens between target and background in different forms of segmentation. To improve the U-Net network architecture, we develop a new architecture named densely connected U-Net (DenseUNet) network in this article. The proposed DenseUNet network adopts a dense block to improve the feature extraction capability and employs a multi-feature fuse block fusing feature maps of different levels to increase the accuracy of feature extraction. In addition, in view of the advantages of the cross entropy and the dice loss functions, a new loss function for the DenseUNet network is proposed to deal with the imbalance between target and background. Finally, we test the proposed DenseUNet network and compared it with the multi-resolutional U-Net (MultiResUNet) and the classic U-Net networks on three different datasets. The experimental results show that the DenseUNet network has significantly performances compared with the MultiResUNet and the classic U-Net networks.

scholarly journals Automated grading of mangoes based on surface defect detection using a combined approach of image segmentation

2020 ◽  
pp. 17-23
Author(s):  
Neeraj Kumari ◽  
Ashutosh Kumar Bhatt ◽  
Rakesh Kumar Dwivedi ◽  
Rajendra Belwal
Keyword(s):  
Feature Extraction ◽  
Feature Selection ◽  
Image Segmentation ◽  
Surface Defect ◽  
Input Image ◽  
Segmentation Technique ◽  
Segmentation Accuracy ◽  
Surface Defect Detection ◽  
Automated Grading ◽  
Processing Accuracy

Image segmentation is an essential and critical step in huge number of applications of image processing. Accuracy of image segmentation influence retrieved information for further processing in classification and other task. In image segmentation algorithms, a single segmentation technique is not sufficient in providing accurate segmentation results in many cases. In this paper we are proposing a combining approach of image segmentation techniques for improving segmentation accuracy. As a case study fruit mango is selected for classification based on surface defect. This classification method consists of three steps: (a) image pre-processing, (b) feature extraction and feature selection and (c) classification of mango. Feature extraction phase is performed on an enhanced input image. In feature selection PCA methodology is used. In classification three classifiers BPNN, Naïve bayes and LDA are used. Proposed image segmentation technique is tested on online dataset and our own collected images database. Proposed segmentation technique performance is compared with existing segmentation techniques. Classification results of BPNN in training and testing phase are acceptable for proposed segmentation technique.

High-content Analysis in Monastrol Suppressor Screens

2011 ◽  
Vol 50 (03) ◽  
pp. 265-272 ◽  
Author(s):  
Y. Ge ◽  
D. Zhang ◽  
X. Zhou ◽  
Z. Zhang
Keyword(s):  
Feature Extraction ◽  
Feature Selection ◽  
Image Segmentation ◽  
Selection Process ◽  
Fluorescent Microscopy ◽  
Back Propagation ◽  
Principal Component ◽  
Current Approach ◽  
Bipolar Cells ◽  
Cell Phenotype

SummaryObjectives: High-content screening (HCS) via automated fluorescent microscopy is a powerful technology for the effective expression of cellular processes. However, HCS will generally produce tremendous image datasets, which leads to difficulties of handling and analyzing. We proposed an automatic classification approach for simultaneous feature extraction and cell phenotype recognition of monoaster and bipolar cells in HCS system.Methods: The proposed approach was composed of image segmentation, feature extraction, and classification. The image segmentation was based on the Laplacian of Gaussian (LoG) edge detection method. For the reduction of noise effect on cellular images, we employed an adaptive threshold in microtubule channel. The principal component analysis was used in the feature selection process. The classification was performed with a back-propagation neural network (BPNN). Using the current approach, the cell phases were distinguished from three-channel acquisitions of cellular images and the numbers of bipolar and monoaster cells were automatically counted.Results: The validity of this approach was examined by the application of screening the response of drug compounds in suppressing Monastrol. Our results indicate that the proposed algorithm could improve the recognition rates of monoaster and bipolar cells to 97.98% and 93.12%, respectively, compared with 97.02% and 86.96% obtained from the same samples by multi-phenotypic mitotic analysis (MMA).Conclusions: We have shown that BPNN is a valuable tool to classify cell phenotype. To further improve the classification performance, more test data, more optimized feature selection approaches, and advanced classifier may be required and will be investigated in future works.

scholarly journals GPU-accelerated image segmentation based on level sets and multiple texture features

2020 ◽  
Author(s):  
Daniel Reska ◽  
Marek Kretowski
Keyword(s):  
Feature Extraction ◽  
Image Segmentation ◽  
Level Set ◽  
High Performance ◽  
Texture Features ◽  
Extraction Methods ◽  
Multi Stage ◽  
Multiple Feature ◽  
Feature Extraction And Selection ◽  
Contour Evolution

Abstract In this paper, we present a fast multi-stage image segmentation method that incorporates texture analysis into a level set-based active contour framework. This approach allows integrating multiple feature extraction methods and is not tied to any specific texture descriptors. Prior knowledge of the image patterns is also not required. The method starts with an initial feature extraction and selection, then performs a fast level set-based evolution process and ends with a final refinement stage that integrates a region-based model. The presented implementation employs a set of features based on Grey Level Co-occurrence Matrices, Gabor filters and structure tensors. The high performance of feature extraction and contour evolution stages is achieved with GPU acceleration. The method is validated on synthetic and natural images and confronted with results of the most similar among the accessible algorithms.

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