Pulse coupled neural network based on Harris hawks optimization algorithm for image segmentation
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Pulse coupled neural network based on Harris hawks optimization algorithm for image segmentation Heming Jia 1 & Xiaoxu Peng 1 & Lifei Kang 1 & Yao Li 1 & Zichao Jiang 1 & Kangjian Sun 1 Received: 6 November 2019 / Revised: 9 June 2020 / Accepted: 15 June 2020 # Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract
Medical image segmentation is a hotspot in the field of image segmentation, and there are many segmentation methods. As a method of image segmentation, pulse coupled neural network (PCNN) has excellent segmentation effect. Of course, it also reduces the efficiency and effect of segmentation because of the complexity of parameter setting and the need for manual setting. This paper presents a method of searching simplified PCNN parameters by using Harris Hawks optimization (HHO) algorithm. For one thing the number of parameters of PCNN is reduced without affecting the segmentation effect, for another the corresponding parameters of PCNN are searched quickly and accurately by intelligent optimization algorithm. Then, image entropy (H) and mutual information entropy (MI) are introduced as fitness functions. The performance of HHO-PCNN is compared with WOA-PCNN, SCAPCNN, SSA-PCNN, PSO-PCNN, GWO-PCNN, MVO-PCNN, Otsu and K-means by performance indicators (UM, CM, Precision, Recall, and Dice). The experimental results verify the superiority of this method in image segmentation. Keywords Image segmentation . Pulse coupled neural network . Harris hawks optimization . Mutual information entropy . Image entropy
1 Introduction The boom in machine vision has spurred the development of many related technologies, image steganography technology [30, 51], image significance detection technology [19, 57], target detection technology [3], visual perception technology [60], image tamper detection [4] and so on. Image segmentation as the lowest level of machine vision engineering applications have been widely used in all walks of life, especially in the diagnosis and analysis of lesions through medical
* Heming Jia [email protected]
1
College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin 150040, China
Multimedia Tools and Applications
image segmentation. Many researchers have explored the field, for instance Guo et al. [11] proposed Otsu thresholding algorithm based on local grid box filter, which shortens the segmentation time and improves the segmentation accuracy. Mutasem [1] proposed the mixture of fuzzy C-means and neutrophils which was used to segment jaw lesions, improved the segmentation accuracy and achieved better segmentation results. J. Hu et al. [17] used Fuzzy C-Means Clustering to segment and recognize fish diseases, which has high segmentation accuracy. Compared with before, the performance of modi-fied intuitionistic fuzzy C-means algorithm (MIFCM) has been improved greatly. Madhukumar [33] made a compareation between K-means and fuzzy C-means for the performance in image segmentation. Although there are also some other segmentation methods,s
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