Read me.txt

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%                
%  Author: Yasmin M. Kassim & Kannappan Palaniappan
%  Copyright(C)2019-2020. Y. Kassim, K. Palaniappan and      
%			  Curators of the University of Missouri, a          
%	             public corporation.
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%  Created by
%  Yasmin M. Kassim & Kannappan Palaniappan
%  Department of Electrical Engineering and Computer Science,
%  University of Missouri-Columbia
%  For more information, contact:
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%      Yasmin Kassim
%      226 Naka Hall (EBW)
%      University of Missouri-Columbia
%      Columbia, MO 65211
%      ymkgz8@mail.missouri.edu
% 
% or
%      Dr. K. Palaniappan
%      205 Naka Hall (EBW)
%      University of Missouri-Columbia
%      Columbia, MO 65211
%      palaniappank@missouri.edu
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Instruction for Running the CNV code:

There are Three main folders in our repository:

Src: contains the algorithm scripts.
Data: contains input images.
Output: contains results, intermediate results and some mat files that are needed by our algorithm. 

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There are two parts for this software in the Src folder, you can skip part1 (Cornea Detection) if you already have your cornea extracted in 400*400 image size dimention. 

Part1 --> Cornea Detection: Extract the cornea from mice raw images using Mask R-CNN

Part2 --> RF CNV Grading: Run the classifier to grade the CNV disease.

In both parts, there are readme file that describes the needed steps. The description is also placed here


%%%%%%%%%% Part1 : Cornea detection 

To get cornea detection

1. Put your raw images in a folder called input, the images should be placed as this example:

ex: input\Extreme\ET_101_Day 21_04.16.2015\image1.png

2. Run Main_cornea_data_preparation.m

This script will prepare your input for Mask R-CNN detection.

3. Setup Mask R-CNN using this website: https://github.com/matterport/Mask_RCNN

Put the mosaic_cornea_weights in log folder, and put your Output\stage_test folder in the same folder with the nucleus example and run nucleus.py 

4. Take the detection masks and place them in 

Output/Output_from_MaskRCNN_masks

To treat the results generated from mask R-CNN by fitting a circle on mask R-CNN results, you should run 5, and the results will be ready in Output/Classify_me_circles folder for RF CNV Grading

5.fit_circles_to_maskrcnn_masks_results.m
 
This script uses Pratt method to fit the cicle

https://www.mathworks.com/matlabcentral/fileexchange/22643-circle-fit-pratt-method


%%%%%%%%% RF CNV Grading

1. Put your output from Cornea detection which is located in Output\Classify_me_circles in a folder called images as this example:

Output\images\test\ET_101_Day 21_04.16.2015\ET_101_Day 21_04.16.2015_Image1_Ex

If you already have your cornea images extracted, you can directly put them inside the folder and run 2

2. Run Grade_cornea_run_me.m