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

Abstract

BackgroundAge-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.MethodsThe 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.ResultsFor 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.ConclusionThe 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.

Automatic diagnosis of macular diseases from OCT volume based on its two-dimensional feature map and convolutional neural network with attention mechanism

“…We compare our methods with several methods, such as that proposed by Santos et al 27 and the voting method of Qiu et al, 17 on the Duke dataset. Santos et al obtained classification results with an SVM classifier using fivefold cross-validation with 100 repetitions.…”

“…Another important technique is true volume-level OCT data classification. [18][19][20][21][22][23][24][25][26][27][28][29] For an OCT volume, it first obtains a global feature representation of the volume and then designs classifiers to recognize it. Venhuizen et al 20,21 obtained the global representation of an OCT volume using clustering and bag-of-word models and classified it using a random forest classifier.…”

“…De Fauw et al 26 obtained a tissue-segmentation map of an OCT volume as a global feature and classified it using a deep learning architecture. Santos et al 27 extracted the global feature of an OCT volume from the perspective of a C-scan using semivariogram and semimadogram functions and recognized it using an SVM. Seebock et al 28 obtained the feature representation of a retinal OCT volume using deep denoising autoencoders to segment anomalous regions and employed clustering to classify it.…”

“…Another important technique is true volume-level OCT data classification. 18 – 29 For an OCT volume, it first obtains a global feature representation of the volume and then designs classifiers to recognize it. Venhuizen et al.…”

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

“…After publication, it was highlighted that the original publication [ 1 ] contained a spelling mistake in the first name of Marcelo Gattas. This was incorrectly captured as Marelo Gattass in the original article which has since been updated.…”