scholarly journals Research on ARKFCM Algorithm Based on Membership Constraint and Bias Field Correction in Neonatal HIE Image Segmentation Method

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Chao Huang ◽  
Jihua Wang
Keyword(s):  
Bias Field ◽  
Sample Point ◽  
Original Image ◽  
Segmentation Method ◽  
Constraint Function ◽  
Bias Field Correction ◽  
Segmented Image ◽  
Segmentation Quality ◽  
Fuzzy Index ◽  
Intrinsic Component

First, this paper presents the algorithm of adaptively regularized kernel-based fuzzy C-means based on membership constraint (G-ARKFCM). Under the idea of competitive learning based on penalizing opponents, a new membership constraint function penalty item is introduced for each sample point in the segmented image, so that the ARKFCM algorithm is no longer limited to the fuzzy index m = 2. Secondly, the multiplicative intrinsic component optimization (MICO) is introduced into G-ARKFCM to obtain the GM-ARKFCM algorithm, which can correct the bias field when segmenting neonatal HIE images. Compared with other algorithms, the GM-ARKFCM algorithm has better segmentation quality and robustness. The GM-ARKFCM algorithm can more completely segment the neonatal ventricles and surrounding white matter and can retain more information of the original image.

scholarly journals A Medical Image Segmentation Method With Anti-Noise and Bias-Field Correction

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 98548-98561 ◽  
Author(s):  
Hong Xu ◽  
Caizeng Ye ◽  
Fan Zhang ◽  
Xuemei Li ◽  
Caiming Zhang
Keyword(s):  
Image Segmentation ◽  
Medical Image ◽  
Bias Field ◽  
Medical Image Segmentation ◽  
Segmentation Method ◽  
Bias Field Correction

Segmentation of Welding Defect Image Based on Exponential Cross Entropy and Improved PCNN

2013 ◽  
Vol 760-762 ◽  
pp. 1467-1471 ◽  
Author(s):  
Zhi Long Ye ◽  
Yi Quan Wu ◽  
Hong Wan ◽  
Zhao Qing Cao
Keyword(s):  
Cross Entropy ◽  
Projection Algorithm ◽  
Threshold Function ◽  
Dynamic Threshold ◽  
Segmentation Method ◽  
Low Contrast ◽  
Segmented Image ◽  
Weighted Matrix ◽  
Defect Image ◽  
Welding Defect

Aiming at welding defect image with complex background and low contrast, a segmentation method of welding defect image based on exponential cross entropy and improved pulse coupled neural network (PCNN) is proposed. Firstly, the area of weld is extracted by gray projection algorithm. Then, link weighted matrix and dynamic threshold function of PCNN are improved. Finally, the exponential cross entropy is calculated as criterion to determine the number of iteration for improved PCNN and get the optimal segmented image. The experimental results are given. Compared with the threshold segmentation method based on exponential cross entropy, the segmentation method based on PCNN and Shannon entropy, the proposed method can achieve better segmented results.

Robust Finger Vein Recognition Based on Deep CNN with Spatial Attention and Bias Field Correction

2021 ◽  
Vol 30 (01) ◽  
pp. 2140005
Author(s):  
Zhe Huang ◽  
Chengan Guo
Keyword(s):  
Spatial Attention ◽  
Transfer Learning ◽  
Learning Strategy ◽  
Bias Field ◽  
The Public ◽  
Finger Vein ◽  
Bias Field Correction ◽  
Training Samples ◽  
Vein Recognition ◽  
Finger Vein Recognition

As one of the biometric information based authentication technologies, finger vein recognition has received increasing attention due to its safety and convenience. However, it is still a challenging task to design an efficient and robust finger vein recognition system because of the low quality of the finger vein images, lack of sufficient number of training samples with image-level annotated information and no pixel-level finger vein texture labels in the public available finger vein databases. In this paper, we propose a novel CNN-based finger vein recognition approach with bias field correction, spatial attention mechanism and a multistage transfer learning strategy to cope with the difficulties mentioned above. In the proposed method, the bias field correction module is to remove the unbalanced bias field of the original images by using a two-dimensional polynomial fitting algorithm, the spatial attention module is to enhance the informative vein texture regions while suppressing the other less informative regions, and the multistage transfer learning strategy is to solve the problem caused by insufficient training for CNN-based model due to lack of labeled training samples in the public finger vein databases. Moreover, several measures, including a label smoothing scheme and data augmentation, are exploited to improve the performance of the proposed method. Extensive experiments have been conducted in the work on three public databases, and the results show that the proposed approach outperforms the existing state-of-the-art methods.

An Image Segmentation Method Based on Adaptability Threshold

2013 ◽  
Vol 734-737 ◽  
pp. 2912-2916
Author(s):  
Hui Li ◽  
Ping He
Keyword(s):  
Image Segmentation ◽  
Strain Measurement ◽  
Maximum Entropy Method ◽  
Entropy Method ◽  
Important Application ◽  
Original Image ◽  
Segmentation Method ◽  
One Dimensional ◽  
The One ◽  
Application Fields

Automation strain measurement of the sheet metal deforming becomes one of the important application fields of computer vision. The algorithm of image segmentation based on adaptability threshold was presented for image segmentation of metal steel. In order to validate the proposed method, it is tested and compared with Ostu method and the one-dimensional maximum entropy method. Experiment results indicate that the method is simple and effective, and has an advantage of reservation of the main features of the original image.

scholarly journals A Multiscale CNN-CRF Framework for Environmental Microorganism Image Segmentation

2020 ◽  
Vol 2020 ◽  
pp. 1-27
Author(s):  
Jinghua Zhang ◽  
Chen Li ◽  
Frank Kulwa ◽  
Xin Zhao ◽  
Changhao Sun ◽  
...  
Keyword(s):  
Image Segmentation ◽  
State Of The Art ◽  
Conditional Random Field ◽  
Segmentation Method ◽  
Recall Accuracy ◽  
Evaluation Indexes ◽  
Segmentation Quality ◽  
Segmentation Approach ◽  
Overall Evaluation

To assist researchers to identify Environmental Microorganisms (EMs) effectively, a Multiscale CNN-CRF (MSCC) framework for the EM image segmentation is proposed in this paper. There are two parts in this framework: The first is a novel pixel-level segmentation approach, using a newly introduced Convolutional Neural Network (CNN), namely, “mU-Net-B3”, with a dense Conditional Random Field (CRF) postprocessing. The second is a VGG-16 based patch-level segmentation method with a novel “buffer” strategy, which further improves the segmentation quality of the details of the EMs. In the experiment, compared with the state-of-the-art methods on 420 EM images, the proposed MSCC method reduces the memory requirement from 355 MB to 103 MB, improves the overall evaluation indexes (Dice, Jaccard, Recall, Accuracy) from 85.24%, 77.42%, 82.27%, and 96.76% to 87.13%, 79.74%, 87.12%, and 96.91%, respectively, and reduces the volume overlap error from 22.58% to 20.26%. Therefore, the MSCC method shows great potential in the EM segmentation field.

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