Abstract
BackgroundImage segmentation is considered an important step in image processing. Fuzzy c-means clustering is one of the common methods of image segmentation. However, this method suffers from drawbacks, such as sensitivity to initial values, entrapment in local optima, and the inability to distinguish objects with similar color intensity. This paper proposes the hybrid Fuzzy c-means clustering and Gray wolf optimization for image segmentation to overcome the shortcomings of Fuzzy c-means clustering. The Gray wolf optimization has a high exploration capability in finding the best solution to the problem, which prevents the entrapment of the algorithm in local optima. In this study, breast cytology images were used to validate the methods, and the results of the proposed method were compared to those of c-means clustering.ResultsFCMGWO has performed better than FCM in separating the nucleus from the other dark objects in the cell. The clustering was validated using Vpc, Vpe, Davies-Bouldin, and Calinski Harabasz criteria. The FCM and FCMGWO methods have a significant difference with respect to the Vpc and Vpe indexes. However, there is no significant difference between the performances of the two clustering methods with respect to the Calinski-Harabasz and Davies-Bouldin indices. The results indicate the better efficacy of the proposed method.ConclusionsThe hybrid FCMGWO algorithm distinguishes the cells better in images with less detail than in images with high detail. However, FCM exhibits unacceptable performance in both low- and high-detail images.
Highlights
Image segmentation is considered an important step in image processing
The points corresponding to the nucleus and other dark objects, such as cytoplasm and red blood cells, have been considered as one cluster by Fuzzy c-means (FCM)
In FCMGWO, these points have been designated as the nucleus, and the other objects have been distinguished as two clusters
Summary
Fuzzy c-means clustering is one of the common methods of image segmentation This method suffers from drawbacks, such as sensi‐ tivity to initial values, entrapment in local optima, and the inability to distinguish objects with similar color intensity. Mohammdian‐khoshnoud et al BMC Molecular and Cell Biology (2022) 23:9 are limited to learning from labeled datasets which are often expensive, time-consuming, and sometimes difficult to produce This issue is more acute in the medical image processing field because the producing high quality datasets requires the effort of experienced and skilled human observers. The main task of evaluation is to measure the similarity between automatic segmentation and reference. It is yet unclear whether a set of general measurements can be used for all segmentation problems
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