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Table 1 Related work comparison

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

Work

Image representation

Preprocessing

Features

Classifier

Volumes

Images are publicly available

Liu et al. [4]

2D

Image warping

Multi-scale spatial pyramid, LBP histogram + PCA

Non-linear Support Vector Machine

457

No

Serrano et al. [7]

2D

Normalization

Haar-Like features and Haralick texture features (curtosis and skewness)

Decision Trees

200

No

Albarrak et al. [8]

3D

Split Bregman Isotropic Total Variation algorithm and a second order polynomial least-square curve fitting for image flattening

Oriented gradient local binary pattern histograms

Bayes network

140

No

Zhang et al. [10]

3D

Bregman Isotropic Total Variation algorithm with a least squares approach

Local binary patterns of three orthogonal planes (LBP-TOP), local phase quantization (LPQ) and multi-scale spatial pyramid (MSSP)

Ensemble of one-class kernel principal component analysis (KPCA) models

140

No

Farsiu et al. [5]

3D

Segmentation of tree retinal layers

Abnormal RPEDC thickness and thinness scores

Generalized linear model regression

384

Yes

Srinivasan et al. [9]

3D

Denoise with BM3D

HOG descriptors

Three linear one-class Support Vector Machines

45

Yes

Venhuizen et al. [11]

2D

First order vertical Gaussian gradient filter

Unsupervised feature learning approach based in patches of images

Random forest classifier

384

Yes

Wang et al. [12]

2D

Multi-scale linear configuration patterns (LCP)

Sequential minimal optimization (SMO)

45

Yes

Sun et al. [13]

2D

Retina aligning and crop SIFT descriptors

 

Three two-class Support Vector Machines (SVM)

45/678 scans

Yes/no

Ravenscroft et al. [15]

2D

Manual segmentation and labelling of choroid

Learnable features by Convolutional Neural Network (CNN)

Neural Network

75

No

Fang et al. [16]

3D

Patch mean removal

PCA features

Extreme learning machine (ELM) classifier

45/54

Yes/no

Karri et al. [17]

2D

RPE estimation based in intensity and BM3D filter is used for noise reduction

Learnable features

Convolutional Neural Network (Transfer learning/GoogLeNet)

45

Yes

Lee et al. [18]

2D

Learnable features

Convolutional Neural Network

100,000 B-scans

No

Kermany et al. [19]

2D

Learnable features

Convolutional Neural Network (Transfer Learning)

207,130 B-scans

Yes