Contrast Media & Molecular Imaging / 2021 / Article / Alg 2

Research Article

Energetic Glaucoma Segmentation and Classification Strategies Using Depth Optimized Machine Learning Strategies

Algorithm 2

Optical disc segmentation process
Input: ROI extracted image from Algorithm 1.
Output: Return the segmented image with associated features.
Step-1: Create an object named ‘im” and Read the ROI extracted image.
Pseudocode:
 im = Red_plane;
Step-2: Create 2 unique objects named ‘cl2 and cl3” and to accumulate the Green and Blue plane values.
Pseudocode:
 cl = imclose(im, ones(9));
 cl2 = imclose(Green_plane, ones(9));
 cl3 = imclose(Blue_plane, ones(9));
Step-3: Accumulate the coefficients of three planes into the respective array variables.
Pseudocode:
 co(:,:,1) = cl; co(:,:,2) = cl2; co(:,:,3) = cl3;
Step-4: Sharpen the estimated image with respect to created coefficients.
Pseudocode:
 C2shap = co; C2shap(:,:,2) = 200;
 ImShar = imsharpen(C2shap);
Step-5: Segmenting the sharpen images based on Step-4.
Pseudocode:
 Define Img_Segment;
 Img_Segment = Segment(co{1,1,1})[ImShar];
Step-6: Return the Optical Disc Segmented image to the HSV plane enhancement stage.
Pseudocode:
 return Img_Segment;

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