Learning fully-connected CRFs for blood vessel segmentation in retinal images

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 8673 LNCS, Issue PART 1, 2014, Pages 634-641

  Orlando, José Ignacio ^a,b,c   Blaschko, Matthew ^a,d  

^a INRIA SACLAY   (France)

^b Facultad de Ciencias Naturales e Instituto M Lillo   (Argentina)

^c UNIVERSIDAD NACIONAL DEL CENTRO DE LA PROVINCIA DE BUENOS AIRES   (Argentina)

^d ECOLE CENTRALE PARIS   (France)

Author keywords

Blood vessel segmentation; Conditional Random Fields; Fundus imaging; Structured Output SVM

Indexed keywords

ALDEHYDES; AUTOMATION; BLOOD VESSELS; EYE PROTECTION; OPHTHALMOLOGY; RANDOM PROCESSES; SUPPORT VECTOR MACHINES; DIAGNOSIS; MEDICAL COMPUTING; MEDICAL IMAGING; POTTS MODEL;

BLOOD VESSEL SEGMENTATION; COMPUTATIONALLY EFFICIENT; CONDITIONAL RANDOM FIELD; DIABETIC RETINOPATHY; DISCRIMINATIVE TRAINING; FUNDUS IMAGING; RETINAL IMAGE ANALYSIS; STRUCTURED OUTPUT SVM;

IMAGE SEGMENTATION;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; COMPUTER ASSISTED DIAGNOSIS; HISTOLOGY; HUMAN; IMAGE ENHANCEMENT; IMAGE SUBTRACTION; METHODOLOGY; REPRODUCIBILITY; RETINA BLOOD VESSEL; RETINOSCOPY; SENSITIVITY AND SPECIFICITY;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; HUMANS; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; RETINAL VESSELS; RETINOSCOPY; SENSITIVITY AND SPECIFICITY; SUBTRACTION TECHNIQUE;

EID: 84909583898     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-319-10404-1_79     Document Type: Conference Paper

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