Enhancing dermatological segmented images using the Sakaguchi kind function coefficient with balancing polynomial
Description
his MATLAB code estimates image segmentation quality metrics without performing actual segmentation, using a novel Geometric Function Theory (GFT) based approach. Coefficient bounds derived from a Sakaguchi-kind bi-univalent function class subordinate to balancing polynomials are used to construct a mathematically justified 3×3 Gaussian filter mask. The code loads a dermoscopic skin lesion image, converts it to a binary ground truth mask using Otsu's thresholding, and applies the Gaussian kernel via 2D convolution to simulate segmentation boundary smoothness. Salt-and-pepper noise is then added to simulate prediction errors, and the noisy image is re-binarized to produce the final predicted mask. The predicted mask is compared pixel-by-pixel with the ground truth to compute TP, TN, FP, and FN, from which eleven quality metrics — Accuracy, Specificity, Precision, Recall, IoU, F1-Score, FPR, FNR, NPV, FDR, and MCC — are derived and reported.
Files
Institutions
- Vellore Institute of Technology UniversityTamil Nadu, Vellore