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. Author manuscript; available in PMC: 2017 Mar 1.
Published in final edited form as: IEEE/ACM Trans Comput Biol Bioinform. 2016 Mar-Apr;13(2):350–364. doi: 10.1109/TCBB.2015.2452916

Fig. 6.

Fig. 6

k-means clustering of EGF-1 and EGF-20 data sets based on morphological features. The markers represent the frames that belong to each data set. The EGF-1 data sets are listed first, followed by the EGF-20 data sets. All the data sets are ordered chronologically from the first image in the set to the last image in that set, where the frame indices for the initial and final image are indicated in the abscissa. We utilize the k-means clustering algorithm with k = 2 clusters. It is observed that cluster C1 is characteristic of smaller buds and shallows clefts, whereas cluster C2 is characteristic of larger buds and deeper clefts. The majority of EGF-1 data sets are present in cluster C2 indicating that this cluster represents larger but fewer buds with deep clefts. The majority of EGF-20 data sets are present in cluster C1 indicating that this cluster represents smaller but multiple buds with shallow clefts. Although, increased EGF stimulates branching morphogenesis by creating more buds, clefts are shallow in comparison to lower EGF concentrations.