Automated macular pathology diagnosis in retinal oct images using multi-scale spatial pyramid and local binary patterns in texture and shape encoding

@article{liu2011automated,
  title={Automated macular pathology diagnosis in retinal OCT images using multi-scale spatial pyramid and local binary patterns in texture and shape encoding},
  author={Liu, Yu-Ying and Chen, Mei and Ishikawa, Hiroshi and Wollstein, Gadi and Schuman, Joel S and Rehg, James M},
  journal={Medical image analysis},
  volume={15},
  number={5},
  pages={748--759},
  year={2011},
  publisher={Elsevier}
}

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.
• 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:

• Canny,
• LBP in MSSP strategy on Canny and original images.

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.

Run the pipeline

From the root directory, launch MATLAB and run:

>> run pipeline/feature-extraction/pipeline_extraction_canny.m
>> run pipeline/feature-extraction/pipeline_extraction_canny_lbp_mssp.m
>> run pipeline/feature-extraction/pipeline_extraction_original_lbp_mssp.m

Classification pipeline

The method for classification used was:

• BoW or majority voting representation,
• Linear or RBF SVM.

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.
• k: this is the number of words for the BoW approach.

Run the pipeline

From the root directory, launch MATLAB and run:

>> run pipeline/feature-classification/pipeline_classifier_maj_vote_linear_SVM.m
>> run pipeline/feature-classification/pipeline_classifier_maj_vote_rbf_SVM.m
>> run pipeline/feature-classification/pipeline_classifier_bow_linear_SVM.m
>> run pipeline/feature-classification/pipeline_classifier_bow_rbf_SVM.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