Segmentation of CT Lung Images Based on 2D Otsu Optimized by Differential Evolution

Image segmentation played a vital role in medical imaging system. With the help of image segmentation pulmonary parenchyma can be detected from multisliced CT images. Pulmonary diseases such as lung cancer, tumor, and mass cells can be detected with 2D Otsu algorithm. 2D Otsu algorithm is a well-known image segmentation method. In CT images segmentation 2D Otsu play a vital role. Main drawback of 2D Otsu method is its computation complexity and computational time. In this paper 2D Otsu algorithm has implemented with Differential Evolution (DE) Algorithm. This results in reducing the computational complexity as well as computational time. Further the results are compared with 2D Otsu and 2D Otsu with PSO, which proves the efficiency of using DE with 2D Otsu.