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Bian X, Pan H, Zhang K, Chen C, Liu P, Shi K. NeDSeM: Neutrosophy Domain-Based Segmentation Method for Malignant Melanoma Images. ENTROPY 2022; 24:e24060783. [PMID: 35741504 PMCID: PMC9222744 DOI: 10.3390/e24060783] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Revised: 05/23/2022] [Accepted: 05/30/2022] [Indexed: 02/07/2023]
Abstract
Skin lesion segmentation is the first and indispensable step of malignant melanoma recognition and diagnosis. At present, most of the existing skin lesions segmentation techniques often used traditional methods like optimum thresholding, etc., and deep learning methods like U-net, etc. However, the edges of skin lesions in malignant melanoma images are gradually changed in color, and this change is nonlinear. The existing methods can not effectively distinguish banded edges between lesion areas and healthy skin areas well. Aiming at the uncertainty and fuzziness of banded edges, the neutrosophic set theory is used in this paper which is better than fuzzy theory to deal with banded edge segmentation. Therefore, we proposed a neutrosophy domain-based segmentation method that contains six steps. Firstly, an image is converted into three channels and the pixel matrix of each channel is obtained. Secondly, the pixel matrixes are converted into Neutrosophic Set domain by using the neutrosophic set conversion method to express the uncertainty and fuzziness of banded edges of malignant melanoma images. Thirdly, a new Neutrosophic Entropy model is proposed to combine the three memberships according to some rules by using the transformations in the neutrosophic space to comprehensively express three memberships and highlight the banded edges of the images. Fourthly, the feature augment method is established by the difference of three components. Fifthly, the dilation is used on the neutrosophic entropy matrixes to fill in the noise region. Finally, the image that is represented by transformed matrix is segmented by the Hierarchical Gaussian Mixture Model clustering method to obtain the banded edge of the image. Qualitative and quantitative experiments are performed on malignant melanoma image dataset to evaluate the performance of the NeDSeM method. Compared with some state-of-the-art methods, our method has achieved good results in terms of performance and accuracy.
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Ouyang Z, Zhao S, Cheng Z, Duan Y, Chen Z, Zhang N, Liang D, Hu Z. Dynamic PET Imaging Using Dual Texture Features. Front Comput Neurosci 2022; 15:819840. [PMID: 35069162 PMCID: PMC8782430 DOI: 10.3389/fncom.2021.819840] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Accepted: 12/10/2021] [Indexed: 11/16/2022] Open
Abstract
Purpose: This study aims to explore the impact of adding texture features in dynamic positron emission tomography (PET) reconstruction of imaging results. Methods: We have improved a reconstruction method that combines radiological dual texture features. In this method, multiple short time frames are added to obtain composite frames, and the image reconstructed by composite frames is used as the prior image. We extract texture features from prior images by using the gray level-gradient cooccurrence matrix (GGCM) and gray-level run length matrix (GLRLM). The prior information contains the intensity of the prior image, the inverse difference moment of the GGCM and the long-run low gray-level emphasis of the GLRLM. Results: The computer simulation results show that, compared with the traditional maximum likelihood, the proposed method obtains a higher signal-to-noise ratio (SNR) in the image obtained by dynamic PET reconstruction. Compared with similar methods, the proposed algorithm has a better normalized mean squared error (NMSE) and contrast recovery coefficient (CRC) at the tumor in the reconstructed image. Simulation studies on clinical patient images show that this method is also more accurate for reconstructing high-uptake lesions. Conclusion: By adding texture features to dynamic PET reconstruction, the reconstructed images are more accurate at the tumor.
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Affiliation(s)
- Zhanglei Ouyang
- School of Physics, Zhengzhou University, Zhengzhou, China
- Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Shujun Zhao
- School of Physics, Zhengzhou University, Zhengzhou, China
| | - Zhaoping Cheng
- Department of PET/CT, The First Affiliated Hospital of Shandong First Medical University, Shandong Provincial Qianfoshan Hospital, Jinan, China
| | - Yanhua Duan
- Department of PET/CT, The First Affiliated Hospital of Shandong First Medical University, Shandong Provincial Qianfoshan Hospital, Jinan, China
| | - Zixiang Chen
- Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Na Zhang
- Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Dong Liang
- Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Zhanli Hu
- Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
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Fan Z, Sun Z, Fang S, Li Y, Liu X, Liang Y, Liu Y, Zhou C, Zhu Q, Zhang H, Li T, Li S, Jiang T, Wang Y, Wang L. Preoperative Radiomics Analysis of 1p/19q Status in WHO Grade II Gliomas. Front Oncol 2021; 11:616740. [PMID: 34295805 PMCID: PMC8290517 DOI: 10.3389/fonc.2021.616740] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2020] [Accepted: 06/17/2021] [Indexed: 11/21/2022] Open
Abstract
Purpose The present study aimed to preoperatively predict the status of 1p/19q based on radiomics analysis in patients with World Health Organization (WHO) grade II gliomas. Methods This retrospective study enrolled 157 patients with WHO grade II gliomas (76 patients with astrocytomas with mutant IDH, 16 patients with astrocytomas with wild-type IDH, and 65 patients with oligodendrogliomas with mutant IDH and 1p/19q codeletion). Radiomic features were extracted from magnetic resonance images, including T1-weighted, T2-weighted, and contrast T1-weighted images. Elastic net and support vector machines with radial basis function kernel were applied in nested 10-fold cross-validation loops to predict the 1p/19q status. Receiver operating characteristic analysis and precision-recall analysis were used to evaluate the model performance. Student’s t-tests were then used to compare the posterior probabilities of 1p/19q co-deletion prediction in the group with different 1p/19q status. Results Six valuable radiomic features, along with age, were selected with the nested 10-fold cross-validation loops. Five features showed significant difference in patients with different 1p/19q status. The area under curve and accuracy of the predictive model were 0.8079 (95% confidence interval, 0.733–0.8755) and 0.758 (0.6879–0.8217), respectively, and the F1-score of the precision-recall curve achieved 0.6667 (0.5201–0.7705). The posterior probabilities in the 1p/19q co-deletion group were significantly different from the non-deletion group. Conclusion Combined radiomics analysis and machine learning showed potential clinical utility in the preoperative prediction of 1p/19q status, which can aid in making customized neurosurgery plans and glioma management strategies before postoperative pathology.
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Affiliation(s)
- Ziwen Fan
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Zhiyan Sun
- Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Shengyu Fang
- Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Yiming Li
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Xing Liu
- Department of Pathology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Yucha Liang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Yukun Liu
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Chunyao Zhou
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Qiang Zhu
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Hong Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Tianshi Li
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Shaowu Li
- Department of Neuroradiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Tao Jiang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Yinyan Wang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Lei Wang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
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Mishra AR, Rani P, Saha A. Single‐valued neutrosophic similarity measure‐based additive ratio assessment framework for optimal site selection of electric vehicle charging station. INT J INTELL SYST 2021. [DOI: 10.1002/int.22523] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Affiliation(s)
- Arunodaya Raj Mishra
- Department of Mathematics Government College Jaitwara Satna Madhya Pradesh India
| | - Pratibha Rani
- Department of Mathematics National Institute of Technology Warangal Telangana India
| | - Abhijit Saha
- Department of Mathematics Techno College of Engineering Agartala Agartala Tripura India
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