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

Algorithm 1

Image acquisition and ROI selection

	Input: OCT Image for Processing.
	Output: Return the ROI Segmented Portion.
	Step-1: Flush out all the cache images and accumulate the input image from the respective system.
	Step-2: Define the variable to acquire the image with specific format inclusions.
	Step-3: Use the function for reading the image called “imread” to obtain the input image into the created object on Step-2.
	Step-4: Crop the selected OCT image by using image cropping function called “imcrop”.
	Step-5: Show the Cropped portion of the image to the user to select the respective ROI area from that noise free image.
	Pseudocode:
	clear all_cache;
	[F_name, p_name] = UI_get_file[{“.jpg; .png; .tif”},“Image_File_Formats”];
	Def I = imread(p_name F_name);
	I = imresize(I,[256 256]);
	out = imcrop(I); img_show(out);
	Step-6: Analyze the Red, Green and Blue (RGB) color portions of the ROI selected and processed image.
	Pseudocode:
	Red_plane = out[:,:,1]; Green_plane = out[:,:,2]; Blue_plane = out[:,:,3];
	Step-7: Plot the selected portions to the user view for identification and validations.
	Pseudocode:
	Subplot(1); image_show[out]; image_head[“ROI”];
	Subplot(2); image_show[Red_plane]; image_head[“Red_Palne”];
	Subplot(3); image_show[Green_plane]; image_head[“Green_Plane”];
	Subplot(4); image_show[Blue_plane]; image_head[“Blue_Plane”];
	ROI_img = image[Red_plane, Green_plane, Blue_plane];
	Step-8: Return the ROI Selected image for further processing.
	Pseudocode:
	return ROI_img;