[go: up one dir, main page]
More Web Proxy on the site http://driver.im/

Gu et al., 2021 - Google Patents

On the performance of lung nodule detection, segmentation and classification

Gu et al., 2021

Document ID
162941851035303239
Author
Gu D
Liu G
Xue Z
Publication year
Publication venue
Computerized Medical Imaging and Graphics

External Links

Snippet

Computed tomography (CT) screening is an effective way for early detection of lung cancer in order to improve the survival rate of such a deadly disease. For more than two decades, image processing techniques such as nodule detection, segmentation, and classification …
Continue reading at www.sciencedirect.com (other versions)

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
    • G06T2207/30—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
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G06T7/0012—Biomedical image inspection
    • 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
    • G06T2207/10104—Positron emission tomography [PET]
    • 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
    • G06K9/6268—Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
    • 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
    • G06K9/52—Extraction of features or characteristics of the image by deriving mathematical or geometrical properties from the whole image
    • G06K9/527—Scale-space domain transformation, e.g. with wavelet analysis
    • 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
    • G06T2207/20112—Image segmentation details
    • 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
    • G06T2207/10116—X-ray image
    • 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
    • G06T2207/20076—Probabilistic image processing
    • 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
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K2209/00—Indexing scheme relating to methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K2209/05—Recognition of patterns in medical or anatomical images
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
    • 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
    • G06T3/0031—Geometric image transformation in the plane of the image, e.g. from bit-mapped to bit-mapped creating a different image for topological mapping of a higher dimensional structure on a lower dimensional surface
    • G06T3/0037—Reshaping or unfolding a 3D tree structure onto a 2D plane

Similar Documents

Publication Publication Date Title
Gu et al. On the performance of lung nodule detection, segmentation and classification
Chugh et al. Survey on machine learning and deep learning applications in breast cancer diagnosis
Yousef et al. A holistic overview of deep learning approach in medical imaging
Yu et al. Convolutional neural networks for medical image analysis: state-of-the-art, comparisons, improvement and perspectives
Malhotra et al. [Retracted] Deep Neural Networks for Medical Image Segmentation
Masood et al. Cloud-based automated clinical decision support system for detection and diagnosis of lung cancer in chest CT
Abdelrahman et al. Convolutional neural networks for breast cancer detection in mammography: A survey
Halder et al. Lung nodule detection from feature engineering to deep learning in thoracic CT images: a comprehensive review
Candemir et al. A review on lung boundary detection in chest X-rays
Shi et al. A deep CNN based transfer learning method for false positive reduction
Al-Antari et al. A fully integrated computer-aided diagnosis system for digital X-ray mammograms via deep learning detection, segmentation, and classification
Liu et al. Multi-view multi-scale CNNs for lung nodule type classification from CT images
Keetha et al. U-Det: A modified U-Net architecture with bidirectional feature network for lung nodule segmentation
Dodia et al. Recent advancements in deep learning based lung cancer detection: A systematic review
Yuan et al. Hybrid-feature-guided lung nodule type classification on CT images
Shaukat et al. Computer-aided detection of lung nodules: a review
Tomassini et al. Lung nodule diagnosis and cancer histology classification from computed tomography data by convolutional neural networks: A survey
Chen et al. LDNNET: towards robust classification of lung nodule and cancer using lung dense neural network
Wu et al. A survey of pulmonary nodule detection, segmentation and classification in computed tomography with deep learning techniques
Kumar et al. Machine learning in medical imaging
Rodríguez et al. Computer aided detection and diagnosis in medical imaging: a review of clinical and educational applications
Lakshmipriya et al. Deep learning techniques in liver tumour diagnosis using CT and MR imaging-A systematic review
Diamant et al. Chest radiograph pathology categorization via transfer learning
Crasta et al. A novel Deep Learning architecture for lung cancer detection and diagnosis from Computed Tomography image analysis
Khalifa et al. Deep learning for image segmentation: a focus on medical imaging