Mean clustering and region growing algorithm to realize medical image processing system - with GUI interface (Matlab)
Medical image processing is an important field in the field of medical imaging. It uses computer technology to digitize, analyze, process and reconstruct medical images to reveal pathological changes and disease characteristics. In medical image processing, mean clustering and region growing algorithms are widely used, and are often used in combination with techniques such as image segmentation, object recognition, and feature extraction.
This article mainly introduces how to use Matlab to write a medical image processing system, including how to use mean clustering and region growing algorithms for image processing, and display the results on the image interface. In addition, we will also come with a GUI interface that allows users to conveniently select images and their parameters and view processing results.
- GUI design
We use Matlab's GUIDE toolbox to design the GUI interface. In the main window we included the following elements: image selection button, two text boxes (for entering parameters for mean clustering and region growing), two buttons (start mean clustering and start region growing) and an axis (for displaying the processed image).
2. Mean clustering
The mean clustering algorithm is an unsupervised clustering algorithm, which can divide the pixels into different regions, and the colors of the pixels in each region are close to each other and different from those in other regions. The basic idea of mean clustering is: first randomly select some pixels as cluster centers, then classify other pixels to the closest cluster centers, and then recalculate the position of each cluster center. Repeat the above steps until the cluster center no longer moves.
The following is our Matlab implementation code:
function [outputImage, clusteredImage