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

Algorithm 3

HSV plane enhancement

	Input: Extract the image features from Algorithm 3.
	Output: Enhanced Image with HSV features.
	Step-1: Create four individual objects called hsv, h, s and v, in which it obtains the HSV values that is converted from RGB image features.
	Pseudocode:
	hsv = rgb2hsv(ImShar);
	h = hsv(:,:,1); s = hsv(:,:,2); v = hsv(:,:,3);
	Step-2: Display all the channel values to the user perspective.
	Pseudocode:
	figure(); subplot(1); img_show(hsv);
	subplot(1);
	img_show(v);
	Step-3: Convolute the image features with respect to “adapthisteq()” equalizer.
	Pseudocode:
	v = adapthisteq(v);
	Step-4: Create an object called “Disc_Image” to store the concatenation values of the HSV proportions.
	Step-5: Enhancing the proportions of image and display it to the user end.
	Pseudocode:
	cEnhance = zeros[size(h,1),size(h,2),3];
	cEnhance(:,:,1) = h; cEnhance(:,:,2) = s; cEnhance(:,:,3) = v;
	img_show[{hsv Disc_Image cEnhance}];
	Step-6: Return the enhanced plane image for optimization.
	Pseudocode:
	return Disc_Image;