Song et al., 2023 - Google Patents
Channel attention generative adversarial network for super-resolution of glioma magnetic resonance imageSong et al., 2023
- Document ID
- 5753068400661287005
- Author
- Song Z
- Qiu D
- Zhao X
- Lin D
- Hui Y
- Publication year
- Publication venue
- Computer Methods and Programs in Biomedicine
External Links
Snippet
Background and objective Glioma is the most common primary craniocerebral tumor caused by the cancelation of glial cells in the brain and spinal cord, with a high incidence and cure rate. Magnetic resonance imaging (MRI) is a common technique for detecting and analyzing …
- 206010018338 Glioma 0 title abstract description 68
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10072—Tomographic images
- G06T2207/10088—Magnetic resonance imaging [MRI]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10024—Color image
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/36—Image preprocessing, i.e. processing the image information without deciding about the identity of the image
- G06K9/46—Extraction of features or characteristics of the image
- G06K9/4604—Detecting partial patterns, e.g. edges or contours, or configurations, e.g. loops, corners, strokes, intersections
- G06K9/4609—Detecting partial patterns, e.g. edges or contours, or configurations, e.g. loops, corners, strokes, intersections by matching or filtering
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20048—Transform domain processing
- G06T2207/20056—Discrete and fast Fourier transform, [DFT, FFT]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30004—Biomedical image processing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20016—Hierarchical, coarse-to-fine, multiscale or multiresolution image processing; Pyramid transform
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6201—Matching; Proximity measures
- G06K9/6202—Comparing pixel values or logical combinations thereof, or feature values having positional relevance, e.g. template matching
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
- G06T7/0012—Biomedical image inspection
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6267—Classification techniques
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T3/00—Geometric image transformation in the plane of the image, e.g. from bit-mapped to bit-mapped creating a different image
- G06T3/40—Scaling the whole image or part thereof
- G06T3/4053—Super resolution, i.e. output image resolution higher than sensor resolution
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration, e.g. from bit-mapped to bit-mapped creating a similar image
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Jing et al. | Hinet: Deep image hiding by invertible network | |
Al-Masni et al. | Multiple skin lesions diagnostics via integrated deep convolutional networks for segmentation and classification | |
CN112767417B (en) | Multi-modal image segmentation method based on cascaded U-Net network | |
Song et al. | Channel attention generative adversarial network for super-resolution of glioma magnetic resonance image | |
Zhu et al. | Arbitrary scale super-resolution for medical images | |
CN113538616B (en) | Magnetic resonance image reconstruction method combining PUGAN with improved U-net | |
Sheng et al. | Second-order ResU-Net for automatic MRI brain tumor segmentation | |
Han et al. | Multi-level U-net network for image super-resolution reconstruction | |
Ding et al. | High-resolution dermoscopy image synthesis with conditional generative adversarial networks | |
Yang et al. | Image super-resolution based on deep neural network of multiple attention mechanism | |
Shahsavari et al. | Proposing a novel Cascade Ensemble Super Resolution Generative Adversarial Network (CESR-GAN) method for the reconstruction of super-resolution skin lesion images | |
Vaiyapuri et al. | Ensemble learning driven computer-aided diagnosis model for brain tumor classification on magnetic resonance imaging | |
CN115496720A (en) | Gastrointestinal cancer pathological image segmentation method based on ViT mechanism model and related equipment | |
Zou et al. | Joint wavelet sub-bands guided network for single image super-resolution | |
Wang et al. | MHAN: Multi-Stage Hybrid Attention Network for MRI reconstruction and super-resolution | |
Rashid et al. | Single MR image super-resolution using generative adversarial network | |
Li et al. | Rethinking diffusion model for multi-contrast mri super-resolution | |
Abd El-Fattah et al. | Deep-learning-based super-resolution and classification framework for skin disease detection applications | |
Yin et al. | Super resolution reconstruction of CT images based on multi-scale attention mechanism | |
Wang et al. | Adaptive denoising for magnetic resonance image based on nonlocal structural similarity and low-rank sparse representation | |
Yang et al. | CT-based transformer model for non-invasively predicting the Fuhrman nuclear grade of clear cell renal cell carcinoma | |
Zhu et al. | Breast Cancer Immunohistochemical Image Generation: a Benchmark Dataset and Challenge Review | |
Chen et al. | Pathological image super-resolution using mix-attention generative adversarial network | |
El-Shafai et al. | Hybrid Single Image Super-Resolution Algorithm for Medical Images. | |
Du et al. | X-ray image super-resolution reconstruction based on a multiple distillation feedback network |