Contrast Media & Molecular Imaging / 2021 / Article / Alg 3

Research Article

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;

Article of the Year Award: Outstanding research contributions of 2020, as selected by our Chief Editors. Read the winning articles.