Paper Info
Title
Multilevel Threshold Image Segmentation With Diffusion Association Slime Mould Algorithm And Renyi'S Entropy For Chronic Obstructive Pulmonary Disease
Abstract
Image segmentation is an essential pre-processing step and is an indispensable part of image analysis. This paper proposes Renyi's entropy multi-threshold image segmentation based on an improved Slime Mould Algorithm (DASMA). First, we introduce the diffusion mechanism (DM) into the original SMA to increase the population's diversity so that the variants can better avoid falling into local optima. The association strategy (AS) is then added to help the algorithm find the optimal solution faster. Finally, the proposed algorithm is applied to Renyi's entropy multilevel threshold image segmentation based on non-local means 2D histogram. The proposed method's effectiveness is demonstrated on the Berkeley segmentation dataset and benchmark (BSD) by comparing it with some well-known algorithms. The DASMA-based multilevel threshold segmentation technique is also successfully applied to the CT image segmentation of chronic obstructive pulmonary disease (COPD). The experimental results are evaluated by image quality metrics, which show the proposed algorithm's extraordinary performance. This means that it can help doctors analyze the lesion tissue qualitatively and quantitatively, improve its diagnostic accuracy and make the right treatment plan. The supplementary material and info about this article will be available at https://aliasgharheidari.com.
Year
DOI
Venue
2021
10.1016/j.compbiomed.2021.104427
COMPUTERS IN BIOLOGY AND MEDICINE
Keywords
DocType
Volume
Meta-heuristic algorithms, Slime mould algorithm, Multi-threshold image segmentation, Renyi's entropy, Chronic obstructive pulmonary disease
Journal
134
ISSN
Citations 
PageRank 
0010-4825
4
0.36
References 
Authors
0
7
Name
Order
Citations
PageRank
Songwei Zhao160.71
Pengjun Wang26211.93
Ali Asghar Heidari350.70
Huiling Chen4121.80
Hamza Turabieh540.70
Majdi Mafarja650.70
Chengye Li71386.88