OpenCV_K-Mean_Clustering_CPP

Hey Folks! I have made an "OpenCV" K-mean clustering program in which you can eliminate the non-populated (less or not absolute neighbors) and can convert the colored image into Grey Scale (black and white). It is used in the medical field to analyze infected skin. install.packages("bmp")

README

Image Segmentation and Evaluation using OpenCV and C++

This project performs image segmentation and evaluation using OpenCV in C++. The code reads grayscale images, performs segmentation using two different methods, and evaluates the results using Dice Coefficient.

Requirements

OpenCV
C++ compiler

Steps to run the code

Set up OpenCV on your system.
Compile the code using a C++ compiler.
Update the image paths in the code to match the location of the images on your system.
Run the compiled executable.

Description of the code

The code contains three main functions:

`int** task1()`

Reads a grayscale image.
Performs image segmentation using a custom algorithm based on labeling and finding the most occurring value (label).
Returns a 2D integer array of the segmented image.

`int** task2()`

Reads a grayscale image.
Performs image segmentation using K-means clustering algorithm.
Returns a 2D integer array of the segmented image.

`int task3()`

Reads a ground truth image.
Calls task1() and task2() to get the segmented images.
Calculates True Positive, False Positive, and False Negative values.
Evaluates the segmentation results using Dice Coefficient.
Prints the evaluation results.

Example output

The code will display the following information:

Rows and columns of the images.
Value of maximum occurrence (label).
False Negative, False Positive, and True Positive values for each task.
Dice Coefficient for each task.

MuhammadAliAhson/K-Mean-Clustering-on-Skin-Disease-using-OpenCV