Waheed et al., 2015 - Google Patents

Hybrid features and mediods classification based robust segmentation of blood vessels

Waheed et al., 2015

Document ID
7140655106383805013
Author
Waheed A
Akram M
Khalid S
Waheed Z
Khan M
Shaukat A
Publication year
Publication venue
Journal of medical systems

External Links

Snippet

Retinal blood vessels are the source to provide oxygen and nutrition to retina and any change in the normal structure may lead to different retinal abnormalities. Automated detection of vascular structure is very important while designing a computer aided diagnostic …
Continue reading at link.springer.com (other versions)

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62Methods or arrangements for recognition using electronic means
    • G06K9/6267Classification techniques
    • G06K9/6268Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/36Image preprocessing, i.e. processing the image information without deciding about the identity of the image
    • G06K9/46Extraction of features or characteristics of the image
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00Image analysis
    • G06T7/0002Inspection of images, e.g. flaw detection
    • G06T7/0012Biomedical image inspection
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/30Subject of image; Context of image processing
    • G06T2207/30004Biomedical image processing
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62Methods or arrangements for recognition using electronic means
    • G06K9/6217Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
    • G06K9/6228Selecting the most significant subset of features
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62Methods or arrangements for recognition using electronic means
    • G06K9/68Methods or arrangements for recognition using electronic means using sequential comparisons of the image signals with a plurality of references in which the sequence of the image signals or the references is relevant, e.g. addressable memory
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/10Image acquisition modality
    • G06T2207/10024Color image
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/20Special algorithmic details
    • G06T2207/20112Image segmentation details
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/00127Acquiring and recognising microscopic objects, e.g. biological cells and cellular parts
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00Image analysis
    • G06T7/40Analysis of texture
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K2209/00Indexing scheme relating to methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F19/00Digital computing or data processing equipment or methods, specially adapted for specific applications
    • G06F19/30Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
    • G06F19/34Computer-assisted medical diagnosis or treatment, e.g. computerised prescription or delivery of medication or diets, computerised local control of medical devices, medical expert systems or telemedicine
    • G06F19/345Medical expert systems, neural networks or other automated diagnosis
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS

Similar Documents

Publication Publication Date Title
Wang et al. Hard exudate detection based on deep model learned information and multi-feature joint representation for diabetic retinopathy screening
Waheed et al. Hybrid features and mediods classification based robust segmentation of blood vessels
Ramasamy et al. Detection of diabetic retinopathy using a fusion of textural and ridgelet features of retinal images and sequential minimal optimization classifier
dos Santos Ferreira et al. Convolutional neural network and texture descriptor-based automatic detection and diagnosis of glaucoma
Van Grinsven et al. Fast convolutional neural network training using selective data sampling: Application to hemorrhage detection in color fundus images
Akram et al. Detection and classification of retinal lesions for grading of diabetic retinopathy
Sopharak et al. Machine learning approach to automatic exudate detection in retinal images from diabetic patients
Akram et al. Identification and classification of microaneurysms for early detection of diabetic retinopathy
Niemeijer et al. Information fusion for diabetic retinopathy CAD in digital color fundus photographs
Yavuz et al. Blood vessel extraction in color retinal fundus images with enhancement filtering and unsupervised classification
Niemeijer et al. Image structure clustering for image quality verification of color retina images in diabetic retinopathy screening
Zhang et al. Retinal vessel segmentation using multi-scale textons derived from keypoints
Zhang et al. Blood vessel segmentation of retinal images based on neural network
Dayana et al. An enhanced swarm optimization-based deep neural network for diabetic retinopathy classification in fundus images
AbdelMaksoud et al. A comprehensive diagnosis system for early signs and different diabetic retinopathy grades using fundus retinal images based on pathological changes detection
Yang et al. Discriminative dictionary learning for retinal vessel segmentation using fusion of multiple features
Kumar et al. IterMiUnet: A lightweight architecture for automatic blood vessel segmentation
Gupta et al. Comparative study of different machine learning models for automatic diabetic retinopathy detection using fundus image
Ravala et al. Automatic diagnosis of diabetic retinopathy from retinal abnormalities: improved Jaya-based feature selection and recurrent neural network
Hatanaka Retinopathy analysis based on deep convolution neural network
Al Sariera et al. Detection and classification of hard exudates in retinal images
Prem et al. Classification of exudates for diabetic retinopathy prediction using machine learning
WO2017046378A1 (en) Method and computer program product for characterizing a retina of a patient from an examination record comprising at least one image of at least a part of the retina
Sudha et al. Cross-Validation Convolution Neural Network-Based Algorithm for Automated Detection of Diabetic Retinopathy.
Wasekar et al. A review on supervised learning methodologies for detection of exudates in diabetic retinopathy