WEEK 6: SEGMENTATION PROCESS

Sunday, 27 March 2022 

This week, I will focus on the segmented image process to detect brain tumors from the MRI images. 

In short, Image segmentation involves converting an image into a collection of regions of pixels that are represented by a mask or a labeled image. By dividing an image into segments, it can process only the important segments of the image instead of processing the entire image. 

Below some techniques that follow this approach are region growing, clustering, and thresholding. 

Region Growing

Region growing is a simple region-based (also classified as a pixel-based) image segmentation method. A popularly used algorithm is active contour, which examines neighboring pixels of initial seed points and determines iteratively whether the pixel neighbors should be added to the region.  This segmentation also can perform on images using the Image Segmenter app. 

Clustering

This technique creates a segmented labeled image using a specific clustering algorithm. Using K-means clustering-based segmentation, imsegkmeans segments an image into K number of clusters. 

Thresholding

Using Otsu’s method, imbinarize performs thresholding on a 2D or 3D grayscale image to create a binary image. To produce a binary image from an RGB color image, use rgb2gray to first convert it to a grayscale image. 

This picture shows the example from this project on how the segmented image is performed, also this approach is thresholding techniques.