Adaptive Artificial Bee Colony for Segmentation of CT lung Images
Sushil Kumar, Tarun Kumar Sharma, Millie Pant · 2012
segmentation of pulmonary parenchyma can be detected from multisliced CT images using image segmentation. It can be modeled as a nonlinear multimodal global optimization problem. The traditional 2D Otsu algorithm, though effective, is quite time consuming for determining the optimum threshold values. In this paper we propose a combination of 2D Otsu method with modified ABC algorithm (called Adaptive ABC or AABC) to reduce the response and computational time. The proposed method has been implemented and tested on three images. Experimental results show the competence of the proposed method for selecting the optimum threshold.