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\section{Conclusion and Recommendation}  Based on the criteria described above, a bigger $k$ would give better results since the variance within clusters decreases. Thus, lessening the chances of mixing healthy and infected pixels in the same cluster. However, more clusters would mean additional computational complexity and longer processing times. K-Means alone has a computational complexity of $O(n^{dk+1}\log{}n)$ \cite{inaba1994applications}, where $d$ $n$  refers to the dimensions (2 in this case $a^*$ number of samples, $k$ refers to the number of clusters,  and $b^*$). $d$ refers to the number of dimensions.  This implies that the complexity increases exponentially as the number of clusters increase.