title: A hybrid flower pollination algorithm based modified randomized location for multi-threshold medical image segmentation.
abstract: Multi-threshold image segmentation is a powerful image processing technique that is used for the preprocessing of pattern recognition and computer vision. However, traditional multilevel thresholding methods are computationally expensive because they involve exhaustively searching the optimal thresholds to optimize the objective functions. To overcome this drawback, this paper proposes a flower pollination algorithm with a randomized location modification. The proposed algorithm is used to find optimal threshold values for maximizing Otsu's objective functions with regard to eight medical grayscale images. When benchmarked against other state-of-the-art evolutionary algorithms, the new algorithm proves itself to be robust and effective through numerical experimental results including Otsu's objective values and standard deviations.
authors.affiliation: College of Information Science and Engineering, Guangxi University for Nationalities, Nanning, China.|Guangxi High School Key Laboratory of Complex System and Computational Intelligence, Guangxi University for Nationalities, Nanning, China.
authors.forename: Rui
authors.initials: R
authors.lastname: Wang
authors.affiliation: College of Information Science and Engineering, Guangxi University for Nationalities, Nanning, China.|Guangxi High School Key Laboratory of Complex System and Computational Intelligence, Guangxi University for Nationalities, Nanning, China.
authors.forename: Yongquan
authors.initials: Y
authors.lastname: Zhou
authors.affiliation: College of Information Science and Engineering, Guangxi University for Nationalities, Nanning, China.|Guangxi High School Key Laboratory of Complex System and Computational Intelligence, Guangxi University for Nationalities, Nanning, China.
authors.forename: Chengyan
authors.initials: C
authors.lastname: Zhao
authors.affiliation: College of Information Science and Engineering, Guangxi University for Nationalities, Nanning, China.|Guangxi High School Key Laboratory of Complex System and Computational Intelligence, Guangxi University for Nationalities, Nanning, China.
authors.forename: Haizhou
authors.initials: H
authors.lastname: Wu
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D035264:Flowers; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D000069550:Machine Learning; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*; D054817:Pollination; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
keywords: Flower pollination algorithm; Otsu method; medical image segmentation; multi-threshold image segmentation; randomized location