Recognition algorithm for assisting ovarian cancer diagnosis from coregistered ultrasound and photoacoustic images: Ex vivo study

Journal of Biomedical Optics

Volumn 17, Issue 12, 2012, Pages

  Alqasemi, Umar ^a   Kumavor, Patrick ^a   Aguirre, Andres ^a   Zhu, Quing ^a  

^a UNIVERSITY OF CONNECTICUT   (United States)

Author keywords

Biomedical image recognition; Coregistered ultrasound and photoacoustic imaging; Linear and nonlinear composite filters; Neural network; Ovarian cancer detection; Support vector machine

Indexed keywords

GENERALIZED LINEAR MODEL; NONLINEAR COMPOSITE; OVARIAN CANCER DIAGNOSIS; OVARIAN CANCERS; PHOTO-ACOUSTIC IMAGING; PHOTOACOUSTIC IMAGE; RECOGNITION ALGORITHM; SENSITIVITY AND SPECIFICITY;

DIAGNOSIS; DISEASES; IMAGE CLASSIFICATION; IMAGE RECOGNITION; NEURAL NETWORKS; ULTRASONIC APPLICATIONS; ULTRASONICS;

SUPPORT VECTOR MACHINES;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; COMPUTER ASSISTED DIAGNOSIS; ELASTOGRAPHY; EQUIPMENT; FEMALE; HUMAN; IMAGE ENHANCEMENT; METHODOLOGY; OVARY TUMOR; PHOTOACOUSTICS; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; ELASTICITY IMAGING TECHNIQUES; FEMALE; HUMANS; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; OVARIAN NEOPLASMS; PATTERN RECOGNITION, AUTOMATED; PHOTOACOUSTIC TECHNIQUES; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY;

EID: 84878023549     PISSN: 10833668     EISSN: 15602281     Source Type: Journal    
DOI: 10.1117/1.JBO.17.12.126003     Document Type: Article

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