Semivariogram and Semimadogram functions as descriptors for AMD diagnosis on SD-OCT topographic maps using Support Vector Machine

Autor: Aristófanes Corrêa Silva, João Dallyson Sousa de Almeida, Steve A. T. Mpinda, Daniel Lima Gomes, Anselmo Cardoso de Paiva, Alex Martins Santos, Geraldo Braz, Adriana P. M. Santos, Marcelo Gattas
Jazyk: angličtina
Rok vydání: 2018
Předmět:
Male
Semivariogram
Support Vector Machine
genetic structures
Computer science
Retinal Pigment Epithelium
01 natural sciences
Macular Degeneration
0302 clinical medicine
Image Processing
Computer-Assisted
Aged
80 and over
Radiological and Ultrasound Technology
medicine.diagnostic_test
General Medicine
Middle Aged
Spelling
Medical images
lcsh:R855-855.5
Female
Tomography
Optical Coherence
lcsh:Medical technology
Biomedical Engineering
Semimadogram
Mistake
Sensitivity and Specificity
Retina
010309 optics
Biomaterials
03 medical and health sciences
Optical coherence tomography
CAD-x
0103 physical sciences
medicine
Humans
Radiology
Nuclear Medicine and imaging
False Positive Reactions
Variogram
Aged
business.industry
Reproducibility of Results
Correction
Pattern recognition
eye diseases
Support vector machine
ROC Curve
030221 ophthalmology & optometry
Artificial intelligence
sense organs
business
Zdroj: BioMedical Engineering OnLine, Vol 17, Iss 1, Pp 1-20 (2018)
BioMedical Engineering
DOI: 10.1186/s12938-018-0592-3
Popis: Background Age-related macular degeneration (AMD) is a degenerative ocular disease that develops by the formation of drusen in the macula region leading to blindness. This condition can be detected automatically by automated image processing techniques applied in spectral domain optical coherence tomography (SD-OCT) volumes. The most common approach is the individualized analysis of each slice (B-Scan) of the SD-OCT volumes. However, it ends up losing the correlation between pixels of neighboring slices. The retina representation by topographic maps reveals the similarity of these structures with geographic relief maps, which can be represented by geostatistical descriptors. In this paper, we present a methodology based on geostatistical functions for the automatic diagnosis of AMD in SD-OCT. Methods The proposed methodology is based on the construction of a topographic map of the macular region. Over the topographic map, we compute geostatistical features using semivariogram and semimadogram functions as texture descriptors. The extracted descriptors are then used as input for a Support Vector Machine classifier. Results For training of the classifier and tests, a database composed of 384 OCT exams (269 volumes of eyes exhibiting AMD and 115 control volumes) with layers segmented and validated by specialists were used. The best classification model, validated with cross-validation k-fold, achieved an accuracy of 95.2% and an AUROC of 0.989. Conclusion The presented methodology exclusively uses geostatistical descriptors for the diagnosis of AMD in SD-OCT images of the macular region. The results are promising and the methodology is competitive considering previous results published in literature.
Databáze: OpenAIRE
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