Machine learning based methods can be categorised into unsupervised and supervised [1], as shown in table 1. Unsupervised machine learning (ML) techniques build their mathematical models from a set of **data** that contain only input without target output labels (segmentation can be referred to as pixel level classification, in that context the target labels are the individual pixel values/ranges in the ground truth mask images. Where, for the case of of unsupervised ML they are not required. An example of unsupervised ML algorithms is the k-means clustering). These techniques unsupervisely discover the **data** pattern and cluster them into segments [18]. For instance, in order to automate the detection of pulmonary tuberculosis (TB) which is caused by Mycobacterium tuberculosis, k-means and self organizing map (SOM) clustering were proposed in the segmentation of the basilli from Ziehl-Neelsen sputum smears [13] and [14]. While in [19], a modified fuzzy divergence clustering method which is based on Cauchy membership function is leveraged in the segmentation of Plasmodium vivax from C channel CMYk color model of images containing the parasites in blood smears. Although unsupervised methods are simple to apply, their ability to learn the pattern of **data** is inadequate in transparent images, which is the case for the weakly visible EM.