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Titlebook: Biomedical Image Analysis; Special Applications Pritpal Singh Book 2024 The Editor(s) (if applicable) and The Author(s), under exclusive li

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發(fā)表于 2025-3-21 16:53:04 | 只看該作者 |倒序瀏覽 |閱讀模式
期刊全稱Biomedical Image Analysis
期刊簡稱Special Applications
影響因子2023Pritpal Singh
視頻videohttp://file.papertrans.cn/189/188046/188046.mp4
發(fā)行地址Provides in-depth description of algorithms.Discusses various biomedical image segmentation and clustering algorithms.Investigates different image analysis methods applied to MRIs and CT scans
學(xué)科分類Brain Informatics and Health
圖書封面Titlebook: Biomedical Image Analysis; Special Applications Pritpal Singh Book 2024 The Editor(s) (if applicable) and The Author(s), under exclusive li
影響因子.This book provides an in-depth study of biomedical image analysis. It reviews and summarizes previous research work in biomedical image analysis and also provides a brief introduction to other computation techniques, such as fuzzy sets, neutrosophic sets, clustering algorithm and fast forward quantum optimization algorithm, focusing on how these techniques can be integrated into different phases of the biomedical image analysis. In particular, this book describes novel methods resulting from the fuzzy sets, neutrosophic sets, clustering algorithm and fast forward quantum optimization algorithm. It also demonstrates how a new quantum-clustering based model can be successfully applied in the context of clustering the COVID-19 CT scans. Thanks to its easy-to-read style and the clear explanations of the models, the book can be used as a concise yet comprehensive reference guide to biomedical image analysis, and will be valuable not only for graduate students, but also for researchers and professionals working for academic, business and government?institutes and medical colleges...?.
Pindex Book 2024
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書目名稱Biomedical Image Analysis影響因子(影響力)




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書目名稱Biomedical Image Analysis網(wǎng)絡(luò)公開度學(xué)科排名




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COVID-19 CT Scan Image Segmentation Using Quantum-Clustering Approach,n be accurately detected. The proposed method is verified and validated with different chest CT scan images of COVID-19 patients. The segmented images obtained using FFQOAK method are compared with various benchmark image segmentation methods. The proposed method achieves mean squared error, peak si
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https://doi.org/10.1007/978-981-10-0173-4py values of gray levels. Finally, an image fusion operation is performed on the segmented images with different thresholds to include all features and identify the location of brain tumor. The fusion images are compared with the segmented images obtained by five different methods, including fuzzy c
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Book 2024also provides a brief introduction to other computation techniques, such as fuzzy sets, neutrosophic sets, clustering algorithm and fast forward quantum optimization algorithm, focusing on how these techniques can be integrated into different phases of the biomedical image analysis. In particular, t
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