Contrast Media & Molecular Imaging / 2021 / Article / Alg 4

Research Article

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

Algorithm 4

Optimization and classification
Input: Obtain the HSV Plane Enhanced Image features from Algorithm 3.
Output: Classified Results with Accuracy ratio.
Step-1: Create an object called cform to store the RGB color lab library functions.
Step-2: Apply the image enhancement ratio to the created object cform on Step-1 and stored that into the new object named lab_hsv.
Step-3: Initiate the dual convolution process to optimize the layers of the lab_hsv.
Step-4: Identify the number of rows and columns presented into the dual convoluted cform object with respect to lab_hsv.
Pseudocode:
 cform = make_c_form(“srgb2lab”);
 lab_hsv = apply_cform(c_Enhance, cform);
 ab = dual(lab_hsv(:,:,2 : 3));
 n_rows = size(ab,1);
 n_cols = size(ab,2);
Step-5: Reshape the generated row and column matrix and store it into the object called ab.
Pseudocode:
 ab = reshape[ab,n_rows n_cols,2];
Step-6: Define a variable called ncolors and assign the constant value to it.
Pseudocode:
 ncolors = 2;
Step-7: Create a cluster index with respect to the color ranges, row and column index ratio, euclidean specifications and the associated replications.
Pseudocode:
 [cluster_index, cluster_size] = enhancekm(kmeans[ab,nColors,“distance,” “sqEuclidean,” “Replicates,” 3]};
Step-8: Reshape the image indexing based on the created cluster index values (Step-7) based on the defined matrices.
Step-9: Assign the respective labels to the generated pixels with respect to RGB color index scheme.
Step-10: Display the resulting images to the user end.
Pseudocode:
 pixel_labels = reshape[cluster_index, n_rows,n_cols];
 Pixel_labels = ∼[pixel_labels > 1];
 img_show[Pixel_labels];
Step-11: Rasie the classification principle to cross check the finalized image into the dataset features to check the given image is considered to be Glaucoma or not as well as the accuracy enumeration is handled through the generated classification law.
Pseudocode:
 Check if input(pixel_labels = = dataset(k));
 segmented_images{k} = color;
 Acc_enum = str2num(Dec{segmented_images{k}});
Step-12: Display the classified image category and raise the alert to indicate the testing image is Glaucoma or not with proper accuracy levels.
 Pseudocode:
 img_show(segmented_images{k});
 return Acc_enum;

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