{"product_id":"artificial-intelligence-in-digital-holographic-imaging-9780470647509","title":"Artificial Intelligence in Digital Holographic","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThis book presents a ground-breaking intelligent system for fast and non-invasive microbial identification using 3D optical imaging methods and high throughput algorithms for automatic analysis of 3D and 4D microscopic image data, as well as analysis of microscopic imaging towards a basic understanding of biological specimens.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e\u003cb\u003ePart I. Digital Holographic Microscopy (DHM) \u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1. Introduction\u003c\/p\u003e \u003cp\u003eReferences\u003c\/p\u003e \u003cp\u003e2. Coherent optical imaging\u003c\/p\u003e \u003cp\u003e2.1 Monochromatic fields and irradiance\u003c\/p\u003e \u003cp\u003e2.2 Analytic expression for Fresnel diffraction\u003c\/p\u003e \u003cp\u003e2.3 Transmittance function of lens\u003c\/p\u003e \u003cp\u003e2.4 Geometrical imaging concepts\u003c\/p\u003e \u003cp\u003e2.5 Coherent imaging theory\u003c\/p\u003e \u003cp\u003eReferences\u003c\/p\u003e \u003cp\u003e3. Lateral and depth resolutions\u003c\/p\u003e \u003cp\u003e3.1 Lateral resolution\u003c\/p\u003e \u003cp\u003e3.2 Depth (or axial) resolution\u003c\/p\u003e \u003cp\u003eReferences\u003c\/p\u003e \u003cp\u003e4. Phase unwrapping\u003c\/p\u003e \u003cp\u003e4.1 Branch cuts\u003c\/p\u003e \u003cp\u003e4.2 Quality-guided path-following algorithms\u003c\/p\u003e \u003cp\u003eReferences\u003c\/p\u003e \u003cp\u003e5. Off-axis digital holographic microscopy\u003c\/p\u003e \u003cp\u003e5.1 Off-axisdigital holographic microscopy designs\u003c\/p\u003e \u003cp\u003e5.2 Digital hologram reconstruction\u003c\/p\u003e \u003cp\u003eReferences\u003c\/p\u003e \u003cp\u003e6. Gabor digital holographic microscopy\u003c\/p\u003e \u003cp\u003e6.1 Introduction\u003c\/p\u003e \u003cp\u003e6.2 Methodology\u003c\/p\u003e \u003cp\u003eReferences\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart II. Deep Learning in DHM Systems\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7. Introduction \u003c\/p\u003e \u003cp\u003eReferences\u003c\/p\u003e \u003cp\u003e8. No-search focus prediction in DHM with deep learning\u003c\/p\u003e \u003cp\u003e8.1 Introduction\u003c\/p\u003e \u003cp\u003e8.2 Materials and methods\u003c\/p\u003e \u003cp\u003e8.3 Experimental results\u003c\/p\u003e \u003cp\u003e8.4 Conclusions\u003c\/p\u003e \u003cp\u003eReferences\u003c\/p\u003e \u003cp\u003e9. Automated phase unwrapping in DHM with deep learning\u003c\/p\u003e \u003cp\u003e9.1 Introduction\u003c\/p\u003e \u003cp\u003e9.2 Deep learning model\u003c\/p\u003e \u003cp\u003e9.3 Unwrapping with deep learning model\u003c\/p\u003e \u003cp\u003e9.4 Conclusions\u003c\/p\u003e \u003cp\u003eReferences\u003c\/p\u003e \u003cp\u003e10. Noise-free phase imaging in Gabor DHM with deep learning\u003c\/p\u003e \u003cp\u003e10.1 Introduction\u003c\/p\u003e \u003cp\u003e10.2 A deep learning model for Gabor DHM\u003c\/p\u003e \u003cp\u003e10.3 Experimental results\u003c\/p\u003e \u003cp\u003e10.4 Discussion\u003c\/p\u003e \u003cp\u003e10.5 Conclusions\u003c\/p\u003e \u003cp\u003eReferences\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart III. Intelligent DHM for Biomedical Applications\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e11. Introduction\u003c\/p\u003e \u003cp\u003eReferences\u003c\/p\u003e \u003cp\u003e12. Red blood cells phase image segmentation\u003c\/p\u003e \u003cp\u003e12.1 Introduction\u003c\/p\u003e \u003cp\u003e12.2 Marker-controlled watershed algorithm\u003c\/p\u003e \u003cp\u003e12.3 Segmentation based on marker-controlled watershed algorithm\u003c\/p\u003e \u003cp\u003e12.4 Experimental results\u003c\/p\u003e \u003cp\u003e12.5 Performance evaluation\u003c\/p\u003e \u003cp\u003e12.6 Conclusions\u003c\/p\u003e \u003cp\u003eReferences\u003c\/p\u003e \u003cp\u003e13. Red blood cells phase image segmentation with deep learning\u003c\/p\u003e \u003cp\u003e13.1 Introduction\u003c\/p\u003e \u003cp\u003e13.2 Fully convolutional neural networks\u003c\/p\u003e \u003cp\u003e13.3 Red blood cells phase image segmentation via deep learning\u003c\/p\u003e \u003cp\u003e13.4 Experimental results\u003c\/p\u003e \u003cp\u003e13.5 Conclusions\u003c\/p\u003e \u003cp\u003eReferences\u003c\/p\u003e \u003cp\u003e14. Automated phenotypic classification of red blood cells\u003c\/p\u003e \u003cp\u003e14.1 Introduction \u003c\/p\u003e \u003cp\u003e14.2 Feature extraction\u003c\/p\u003e \u003cp\u003e14.3 Pattern recognition neural network\u003c\/p\u003e \u003cp\u003e14.4 Experimental results and discussion\u003c\/p\u003e \u003cp\u003e14.5 Conclusions\u003c\/p\u003e \u003cp\u003eReferences\u003c\/p\u003e \u003cp\u003e15. Automated analysis of red blood cell storage lesions\u003c\/p\u003e \u003cp\u003e15.1 Introduction\u003c\/p\u003e \u003cp\u003e15.2 Quantitative analysis of red blood cell 3D morphological changes\u003c\/p\u003e \u003cp\u003e15.3 Experimental results and discussion\u003c\/p\u003e \u003cp\u003e15.4 Conclusions\u003c\/p\u003e \u003cp\u003eReferences\u003c\/p\u003e \u003cp\u003e16. Automated red blood cells classification with deep learning\u003c\/p\u003e \u003cp\u003e16.1 Introduction\u003c\/p\u003e \u003cp\u003e16.2 Proposed deep learning model\u003c\/p\u003e \u003cp\u003e16.3 Experimental results\u003c\/p\u003e \u003cp\u003e16.4 Conclusions\u003c\/p\u003e \u003cp\u003eReferences\u003c\/p\u003e \u003cp\u003e17. High-throughput label-free cell counting with deep neural networks\u003c\/p\u003e \u003cp\u003e17.1 Introduction\u003c\/p\u003e \u003cp\u003e17.2 Materials and methods\u003c\/p\u003e \u003cp\u003e17.3 Experimental results\u003c\/p\u003e \u003cp\u003e17.4 Conclusions\u003c\/p\u003e \u003cp\u003eReferences\u003c\/p\u003e \u003cp\u003e18. Automated tracking of temporal displacements of red blood cells\u003c\/p\u003e \u003cp\u003e18.1 Introduction\u003c\/p\u003e \u003cp\u003e18.2 Mean-shift tracking algorithm\u003c\/p\u003e \u003cp\u003e18.3 Kalman filter\u003c\/p\u003e \u003cp\u003e18.4 Procedure for single RBC tracking\u003c\/p\u003e \u003cp\u003e18.5 Experimental results\u003c\/p\u003e \u003cp\u003e18.6 Conclusions\u003c\/p\u003e \u003cp\u003eReferences\u003c\/p\u003e \u003cp\u003e19. Automated quantitative analysis of red blood cells dynamics\u003c\/p\u003e \u003cp\u003e19.1 Introduction\u003c\/p\u003e \u003cp\u003e19.2 Red blood cell parameters\u003c\/p\u003e \u003cp\u003e19.3 Quantitative analysis of red blood cell fluctuations\u003c\/p\u003e \u003cp\u003e19.4 Conclusions\u003c\/p\u003e \u003cp\u003eReferences\u003c\/p\u003e \u003cp\u003e20. Quantitative analysis of red blood cells during temperature elevation\u003c\/p\u003e \u003cp\u003e20.1 Introduction\u003c\/p\u003e \u003cp\u003e20.2 Red blood cell sample preparations\u003c\/p\u003e \u003cp\u003e20.3 Experimental results\u003c\/p\u003e \u003cp\u003e20.4 Conclusions\u003c\/p\u003e \u003cp\u003eReferences\u003c\/p\u003e \u003cp\u003e21. Automated measurement of cardiomyocytes dynamics with DHM\u003c\/p\u003e \u003cp\u003e21.1 Introduction\u003c\/p\u003e \u003cp\u003e21.2 Cell culture and imaging\u003c\/p\u003e \u003cp\u003e21.3 Automated analysis of cardiomyocytes dynamics\u003c\/p\u003e \u003cp\u003e21.4 Conclusions\u003c\/p\u003e \u003cp\u003eReferences\u003c\/p\u003e \u003cp\u003e22. Automated analysis of cardiomyocytes with deep learning\u003c\/p\u003e \u003cp\u003e22.1 Introduction\u003c\/p\u003e \u003cp\u003e22.2 Region of interest identification with dynamic beating activity analysis\u003c\/p\u003e \u003cp\u003e22.3 Deep neural network for cardiomyocytes image segmentation\u003c\/p\u003e \u003cp\u003e22.4 Experimental results\u003c\/p\u003e \u003cp\u003e22.5 Conclusions\u003c\/p\u003e \u003cp\u003eReferences\u003c\/p\u003e \u003cp\u003e23. Automatic quantification of drug-treated cardiomyocytes with DHM\u003c\/p\u003e \u003cp\u003e23.1 Introduction\u003c\/p\u003e \u003cp\u003e23.2 Materials and methods\u003c\/p\u003e \u003cp\u003e23.3 Experimental results and discussion\u003c\/p\u003e \u003cp\u003e23.4 Conclusions\u003c\/p\u003e \u003cp\u003eReferences\u003c\/p\u003e \u003cp\u003e24. Analysis of cardiomyocytes with holographic image-based tracking\u003c\/p\u003e \u003cp\u003e24.1 Introduction\u003c\/p\u003e \u003cp\u003e24.2 Materials and methods\u003c\/p\u003e \u003cp\u003e24.3 Experimental results and discussion\u003c\/p\u003e \u003cp\u003e24.4 Conclusions\u003c\/p\u003e \u003cp\u003eReferences\u003c\/p\u003e \u003cp\u003e25. Conclusion and future work\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":49525383790935,"sku":"9780470647509","price":108.9,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780470647509.jpg?v=1731860309","url":"https:\/\/bookcurl.com\/products\/artificial-intelligence-in-digital-holographic-imaging-9780470647509","provider":"Book Curl","version":"1.0","type":"link"}