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Classification of SD-OCT volumes for DME detection: an anomaly detection approach

@proceeding{sankar2016classification,
author = {Sankar, S. and Sidib\'{e}, D. and Cheung, Y. and Wong, T. Y. and Lamoureux, E. and Milea, D. and Meriaudeau, F.},
title = {Classification of SD-OCT volumes for DME detection: an anomaly detection approach},
journal = {Proc. SPIE},
volume = {9785},
pages = {97852O-97852O-6},
year = {2016}
}

How to use the pipeline?

Pre-processing pipeline

The follwoing pre-processing routines were applied:

  • Flattening,
  • Cropping.

Data variables

In the file pipeline/feature-preprocessing/pipeline_preprocessing.m, you need to set the following variables:

  • data_directory: this directory contains the orignal SD-OCT volume. The format used was .img.
  • store_directory: this directory corresponds to the place where the resulting data will be stored. The format used was .mat.

Algorithm variables

The variables which are not indicated in the inital publication and that can be changed are:

  • x_size, y_size, z_size: the original size of the SD-OCT volume. It is needed to open .img file.
  • kernelratio, windowratio, filterstrength: the NLM parameters.
  • h_over_rpe, h_under_rpe, width_crop: the different variables driving the cropping.
  • thres_method, thres_val: method to threshold and its associated value to binarize the image.
  • gpu_enable: method to enable GPU.
  • median_sz: size of the kernel when applying the median filter.
  • se_op, se_cl: size of the kernel when applying the closing and opening operations.

Run the pipeline

From the root directory, launch MATLAB and run:

>> run pipeline/feature-preprocessing/pipeline_preprocessing.m

Extraction pipeline

For this pipeline, the following features were extracted:

  • PCA on vectorized B-scans.

Data variables

In the file pipeline/feature-extraction/pipeline_extraction.m, you need to set the following variables:

  • data_directory: this directory contains the pre-processed SD-OCT volume. The format used was .mat.
  • store_directory: this directory corresponds to the place where the resulting data will be stored. The format used was .mat.
  • pca_compoments: this the number of components to keep when reducing the dimension by PCA.

Run the pipeline

From the root directory, launch MATLAB and run:

>> run pipeline/feature-extraction/pipeline_extraction.m

Classification pipeline

The method for classification used was:

  • GMM modelling.

Data variables

In the file pipeline/feature-preprocessing/pipeline_classifier.m, you need to set the following variables:

  • data_directory: this directory contains the feature extracted from the SD-OCT volumes. The format used was .mat.
  • store_directory: this directory corresponds to the place where the resulting data will be stored. The format used was .mat.
  • gt_file: this is the file containing the label for each volume. You will have to make your own strategy.
  • gmm_k: this is the number of mixture components of the GMM.
  • pca_components: this is the number of components of the PCA used in the extraction.
  • mahal_thresh: the treshold to use to consider a B-scan as abnormal or not.
  • n_slices_thres: the minimum number of abnormal slices to consider the volume as DME.

Run the pipeline

From the root directory, launch MATLAB and run:

>> run pipeline/feature-classification/pipeline_classifier.m

Validation pipeline

Data variables

In the file pipeline/feature-validation/pipeline_validation.m, you need to set the following variables:

  • data_directory: this directory contains the classification results. The format used was .mat.
  • gt_file: this is the file containing the label for each volume. You will have to make your own strategy.

Run the pipeline

From the root directory, launch MATLAB and run:

>> run pipeline/feature-validation/pipeline_validation.m