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Artificial Intelligence in Medical Image Processing and Segmentation
| Artificial Intelligence in Medical Image Processing and Segmentation |
| Pubbl/distr/stampa | MDPI - Multidisciplinary Digital Publishing Institute, 2023 |
| Descrizione fisica | 1 online resource (348 p.) |
| Soggetto topico |
History of engineering & technology
Technology: general issues |
| Soggetto non controllato |
2D ultrasound image
2D/3D registration 3D virtual reconstruction ALO artificial hummingbird algorithm artificial intelligence artificial neural network (ANN) attention mechanism auto-segmentation automatic volume measurement breast cancer breast density CAD canonical correlation analysis (CCA) carbon ion radiotherapy CBCT cervical cancer cervical net children CNN comparison computer vision applications convolutional neural network Convolutional Neural Networks Cranio-Maxillofacial surgery CT DarkNet-19 deep learning deep learning structures DICOM directional total variation disease discrimination accuracy dual-energy CT edge computing ensemble learning feature fusion feature selection fundus image GAN Grad-CAM hematoxylin eosin histopathological histopathology histopathology images image enhancement image inpainting image normalization image preprocessing image registration image segmentation image-guided radiotherapy in-house instance segmentation k-nearest neighbour (KNN) lesion segmentation limited-angular range loss function magnetic resonance imaging mandible mask-transformer-based networks medical image analysis medical imaging mitotic nuclei classification MobileNet mp-MRI MRI MRI guidance MRI-only multi-contrast MRI NasNet neuroimaging nuclei detection nuclei segmentation OCT orthogonal X-ray osteoarthritis ovarian tumor PA panoptic segmentation panoramic radiographs pap smear particle therapy patch size PCA PCNSL performance comparisons prostate cancer prostate segmentation pyramidal network radiomics random forest (RF) rare neurodevelopmental disorder rare tumor ResNet-101 safranin O fast green scale-adaptive segmentation semantic segmentation shuffle net ShuffleNet software support vector machine support vector machine (SVM) synthetic CT teeth segmentation textural tooth disease recognition tuberous sclerosis complex two-step method U-Net ultrasound bladder scanner urinary disease |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910743276403321 |
| MDPI - Multidisciplinary Digital Publishing Institute, 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Image Processing Using FPGAs / Donald Bailey
| Image Processing Using FPGAs / Donald Bailey |
| Autore | Bailey Donald |
| Pubbl/distr/stampa | MDPI - Multidisciplinary Digital Publishing Institute, 2019 |
| Descrizione fisica | 1 electronic resource (204 p.) |
| Soggetto topico | Information technology industries |
| Soggetto non controllato |
nuclei detection
System-on-Chip FPGA K-Means hardware acceleration image analysis perceptual coding line buffer heterogeneous computing window filters processor architectures hardware accelerators stream processing embedded systems image processing pipeline image processing generalized Laplacian of Gaussian filter background estimation real-time systems FPGA implementation hardware architecture compression image borders memory zig-zag scan histopathology just-noticeable difference (JND) memory management downsampling image segmentation feature extraction design mean Shift clustering high-throughput segmentation streaming architecture power D-SWIM hardware/software co-design high-level synthesis contrast masking texture detection pipeline field programmable gate array (FPGA) JPEG-LS low-latency connected components analysis luminance masking field programmable gate arrays (FPGA) |
| ISBN |
9783038979197
3038979198 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910346693003321 |
Bailey Donald
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| MDPI - Multidisciplinary Digital Publishing Institute, 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||