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;