Contrast Media & Molecular Imaging / 2021 / Article / Alg 1

Research Article

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;

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