Multilevel Threshold Based Gray Scale Image Segmentation using Cuckoo Search
This is an incremental improvement for image processing applications, focusing on optimizing segmentation quality in a specific domain.
The paper tackles the problem of gray scale image segmentation by using multilevel thresholding with Cuckoo Search to select optimal threshold values, resulting in segmentation quality measured by MSE and PSNR metrics.
Image Segmentation is a technique of partitioning the original image into some distinct classes. Many possible solutions may be available for segmenting an image into a certain number of classes, each one having different quality of segmentation. In our proposed method, multilevel thresholding technique has been used for image segmentation. A new approach of Cuckoo Search (CS) is used for selection of optimal threshold value. In other words, the algorithm is used to achieve the best solution from the initial random threshold values or solutions and to evaluate the quality of a solution correlation function is used. Finally, MSE and PSNR are measured to understand the segmentation quality.