Fuzzy and elitist cuckoo search based microscopic image segmentation approach

Abstract

Microscopic images have the potential to talk about much precious information about the internal structure of living organisms. But the naked eye is not always efficient enough to explore various hidden information from the microscopic images and here the need for automated image analysis tools comes into the picture. The interval type 2 fuzzy C-Means clustering and cuckoo search-based microscopic image segmentation approach is proposed in this work. The proposed algorithm will be known as the EFECS (Enhanced Fuzzy Elitist Cuckoo Search algorithm) approach that overcomes the dependency on the initial selection of the cluster centers by using the randomness of the EFECS method. EFECS method uses interval type 2 fuzzy membership to update the cluster centers. The proposed EFECS method is compared with some well-known methods to prove its superiority. The results are verified using both qualitative and quantitative manner. Experimental results established the superiority and the real-life applicability of the proposed EFECS algorithm. On average (for 150 images), the proposed approach archives 0.901537 DB index value (5 clusters), 0.629407 XB index value (5 clusters), 2.84774 Dunn index value (5 clusters), and 4.368482 β index value (7 clusters) that outperforms its nearest competitors CS with 0.904246 DB index value (7 clusters), ACO with 0.763519 XB index value (9 clusters), CS with 2.59191 Dunn index value (5 clusters), and ACO with 4.24919 β index value (7 clusters) respectively. Moreover, on average, the proposed approach achieves MSE values 297.8501535 (3 clusters), 303.4967502 (5 clusters), 296.6295076 (7 clusters), 311.9109645 (9 clusters) that also outperforms other approaches.