Multilevel Multiobjective Particle Swarm Optimization Guided Superpixel Algorithm for Histopathology Image Detection and Segmentation

• Published in: Journal of imaging Vol. 9; no. 4; p. 78
• Main Authors: Kanadath, Anusree, Angel Arul Jothi, J., Urolagin, Siddhaling
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 29.03.2023; MDPI
• Subjects: Algorithms; Cancer; Clustering; Coefficients; Computer-aided medical diagnosis; Datasets; Deep learning; Diagnosis; Evolutionary algorithms; Genetic algorithms; Histology, Pathological; Histopathology; Image analysis; Image detection; Image processing; Image segmentation; Medical imaging; Methods; multiobjective algorithms; Multiple objective analysis; nature-inspired algorithms; Optimization algorithms; Particle swarm optimization; Sorting algorithms; Swarm intelligence; Technology application; thresholding; Wavelet transforms; United Arab Emirates; histopathology; nature-inspired algorithms; thresholding; multiobjective algorithms; particle swarm optimization; image segmentation
• ISSN: 2313-433X; 2313-433X
• DOI: 10.3390/jimaging9040078

Histopathology image analysis is considered as a gold standard for the early diagnosis of serious diseases such as cancer. The advancements in the field of computer-aided diagnosis (CAD) have led to the development of several algorithms for accurately segmenting histopathology images. However, the application of swarm intelligence for segmenting histopathology images is less explored. In this study, we introduce a Multilevel Multiobjective Particle Swarm Optimization guided Superpixel algorithm (MMPSO-S) for the effective detection and segmentation of various regions of interest (ROIs) from Hematoxylin and Eosin (H&E)-stained histopathology images. Several experiments are conducted on four different datasets such as TNBC, MoNuSeg, MoNuSAC, and LD to ascertain the performance of the proposed algorithm. For the TNBC dataset, the algorithm achieves a Jaccard coefficient of 0.49, a Dice coefficient of 0.65, and an F-measure of 0.65. For the MoNuSeg dataset, the algorithm achieves a Jaccard coefficient of 0.56, a Dice coefficient of 0.72, and an F-measure of 0.72. Finally, for the LD dataset, the algorithm achieves a precision of 0.96, a recall of 0.99, and an F-measure of 0.98. The comparative results demonstrate the superiority of the proposed method over the simple Particle Swarm Optimization (PSO) algorithm, its variants (Darwinian particle swarm optimization (DPSO), fractional order Darwinian particle swarm optimization (FODPSO)), Multiobjective Evolutionary Algorithm based on Decomposition (MOEA/D), non-dominated sorting genetic algorithm 2 (NSGA2), and other state-of-the-art traditional image processing methods.