Medical image segmentation by fuzzy logic techniques

2002 ◽  
Author(s):  
Y. Hata ◽  
S. Kobashi ◽  
S. Hirano
Keyword(s):  
Fuzzy Logic ◽  
Image Segmentation ◽  
Medical Image ◽  
Medical Image Segmentation

Fuzzy logic and local features based medical image segmentation

2013 ◽  
Author(s):  
Umer Javed ◽  
M. Mohsin Riaz ◽  
Muhammad Rizwan Khokher ◽  
Abdul Ghafoor ◽  
Tanveer A. Cheema
Keyword(s):  
Fuzzy Logic ◽  
Image Segmentation ◽  
Medical Image ◽  
Local Features ◽  
Medical Image Segmentation

Medical Image Thresholding Using Genetic Algorithm and Fuzzy Membership Functions

2019 ◽  
Vol 8 (4) ◽  
pp. 39-59
Author(s):  
Shashwati Mishra ◽  
Mrutyunjaya Panda
Keyword(s):  
Genetic Algorithm ◽  
Fuzzy Logic ◽  
Image Segmentation ◽  
Medical Image ◽  
Medical Image Segmentation ◽  
Hybrid Technique ◽  
Membership Functions ◽  
Novel Approach ◽  
Fitness Value ◽  
Sensitivity Specificity

Thresholding is one of the important steps in image analysis process and used extensively in different image processing techniques. Medical image segmentation plays a very important role in surgery planning, identification of tumours, diagnosis of organs, etc. In this article, a novel approach for medical image segmentation is proposed using a hybrid technique of genetic algorithm and fuzzy logic. Fuzzy logic can handle uncertain and imprecise information. Genetic algorithms help in global optimization, gives good results in noisy environments and supports multi-objective optimization. Gaussian, trapezoidal and triangular membership functions are used separately for calculating the entropy and finding the fitness value. CPU time, Root Mean Square Error, sensitivity, specificity, and accuracy are calculated using the three membership functions separately at threshold levels 2, 3, 4, 5, 7 and 9. MRI images are considered for applying the proposed method and the results are analysed. The experimental results obtained prove the effectiveness and efficiency of the proposed method.

Medical Image Thresholding Using Genetic Algorithm and Fuzzy Membership Functions

2021 ◽  
pp. 896-916
Author(s):  
Shashwati Mishra ◽  
Mrutyunjaya Panda
Keyword(s):  
Genetic Algorithm ◽  
Fuzzy Logic ◽  
Image Segmentation ◽  
Medical Image ◽  
Medical Image Segmentation ◽  
Hybrid Technique ◽  
Membership Functions ◽  
Novel Approach ◽  
Sensitivity Specificity ◽  
Processing Techniques

Thresholding is one of the important steps in image analysis process and used extensively in different image processing techniques. Medical image segmentation plays a very important role in surgery planning, identification of tumours, diagnosis of organs, etc. In this article, a novel approach for medical image segmentation is proposed using a hybrid technique of genetic algorithm and fuzzy logic. Fuzzy logic can handle uncertain and imprecise information. Genetic algorithms help in global optimization, gives good results in noisy environments and supports multi-objective optimization. Gaussian, trapezoidal and triangular membership functions are used separately for calculating the entropy and finding the fitness value. CPU time, Root Mean Square Error, sensitivity, specificity, and accuracy are calculated using the three membership functions separately at threshold levels 2, 3, 4, 5, 7 and 9. MRI images are considered for applying the proposed method and the results are analysed. The experimental results obtained prove the effectiveness and efficiency of the proposed method.

Design of Superpiexl U-Net Network for Medical Image Segmentation

2019 ◽  
Vol 31 (6) ◽  
pp. 1007 ◽  
Author(s):  
Haiou Wang ◽  
Hui Liu ◽  
Qiang Guo ◽  
Kai Deng ◽  
Caiming Zhang
Keyword(s):  
Image Segmentation ◽  
Medical Image ◽  
Medical Image Segmentation

scholarly journals A Self-Spatial Adaptive Weighting Based U-Net for Image Segmentation

Electronics ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 348
Author(s):  
Choongsang Cho ◽  
Young Han Lee ◽  
Jongyoul Park ◽  
Sangkeun Lee
Keyword(s):  
Image Segmentation ◽  
Medical Image ◽  
Medical Image Segmentation ◽  
Feature Maps ◽  
Data Set ◽  
Feature Map ◽  
Adaptive Weighting ◽  
Spatially Adaptive ◽  
Wide Range ◽  
Decoder Architecture

Semantic image segmentation has a wide range of applications. When it comes to medical image segmentation, its accuracy is even more important than those of other areas because the performance gives useful information directly applicable to disease diagnosis, surgical planning, and history monitoring. The state-of-the-art models in medical image segmentation are variants of encoder-decoder architecture, which is called U-Net. To effectively reflect the spatial features in feature maps in encoder-decoder architecture, we propose a spatially adaptive weighting scheme for medical image segmentation. Specifically, the spatial feature is estimated from the feature maps, and the learned weighting parameters are obtained from the computed map, since segmentation results are predicted from the feature map through a convolutional layer. Especially in the proposed networks, the convolutional block for extracting the feature map is replaced with the widely used convolutional frameworks: VGG, ResNet, and Bottleneck Resent structures. In addition, a bilinear up-sampling method replaces the up-convolutional layer to increase the resolution of the feature map. For the performance evaluation of the proposed architecture, we used three data sets covering different medical imaging modalities. Experimental results show that the network with the proposed self-spatial adaptive weighting block based on the ResNet framework gave the highest IoU and DICE scores in the three tasks compared to other methods. In particular, the segmentation network combining the proposed self-spatially adaptive block and ResNet framework recorded the highest 3.01% and 2.89% improvements in IoU and DICE scores, respectively, in the Nerve data set. Therefore, we believe that the proposed scheme can be a useful tool for image segmentation tasks based on the encoder-decoder architecture.

Multi-Encoder Parse-Decoder Network for Sequential Medical Image Segmentation

2021 ◽  
Author(s):  
Dachuan Shi ◽  
Ruiyang Liu ◽  
Linmi Tao ◽  
Zuoxiang He ◽  
Li Huo
Keyword(s):  
Image Segmentation ◽  
Medical Image ◽  
Medical Image Segmentation
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