Image Segmentation of Breast Cancer Histopathology Images Using PSO-Based Clustering Technique
- 20 Downloads
Image segmentation has key influence in numerous medical imaging uses. An image segmentation model that is based on the particle swarm optimizer (PSO) is developed in this paper for breast cancer histopathology images of different magnification levels (40X, 100X, 200X and 400X), thus simplifying image representation and making it meaningful and easier for future analysis. As lower the magnification level, the bigger is the field of view and as it can provide greater detail, thus more time and care must be taken to use such images. Thus, finding a segmentation method that works equally well for all zoom levels of images is a big challenge. To explicate the better performance of the proposed method and its applicability on breast cancer images, results of applications and tests are augmented which shows PSO image clustering approach using intra-cluster distance as an optimization function, performs superior to cutting edge strategies, namely K-means and genetic algorithm (GA). The algorithms, when given specified number of clusters, find the centroids, thus grouping similar image primitives. The influence of different estimations of PSO control parameters on execution is additionally outlined.
KeywordsUnsupervised clustering Evolutionary algorithms PSO GA K-means Color segmentation
I would like to deeply express my thanks to my guide for giving valuable suggestions and her kind support.
- 2.Tosta TAA, Neves LA, do Nascimento MZ (2017) Segmentation methods of H&E-stained histological images of lymphoma: a review. Inf Med 35–43Google Scholar
- 3.Gelasca, ED, Obara B, Fedorov D, Kvilekval K, Manjunath BS (2009) A biosegmentation benchmark for evaluation of bioimage analysis methods. BMC Bioinf 10(1)Google Scholar
- 4.MITOS, ICPR (2012) Contest, IPAL UMI CNRS Lab Std., [Online]. Available: http://ipal.cnrs.fr/ICPR2012/?q=node/5
- 7.Ab Wahab MN, Nefti-Meziani S, Atyabi A (2015) A comprehensive review of swarm optimization algorithms. PLoS One 10.5Google Scholar
- 8.Xu Y et al (2012) Multiple clustered instance learning for histopathology cancer image classification, segmentation and clustering. In: 2012 IEEE conference on computer vision and pattern recognition (CVPR). IEEEGoogle Scholar
- 14.Dhal KG, Fister I Jr, Das A, Ray S, Das S (2018) Breast histopathology image clustering using cuckoo search algorithm. Proceedings of the 5th student computer science research conference, pp 47–54Google Scholar
- 16.Ait-Aoudia S, Guerrout EH, Mahiou R (2014) Medical image segmentation using particle swarm optimization. In: 2014 18th international conference on information visualisation (IV), pp 287–291Google Scholar
- 17.Van der Merwe DW, Engelbrecht AP (2003) Data clustering using particle swarm optimization. In: The 2003 Congress on evolutionary computation. CEC’03, vol 1, pp 215–220. IEEEGoogle Scholar