I suggest Tree Data Structure. A tree data structure is a powerful tool for organizing data objects based on keys. It is equally useful for organizing multiple data objects in terms of hierarchical relationships (think of a ``family tree'', where the children are grouped under their parents in the tree).

Basically Tree is a data structure which is used to arrange the huge amount of data at one place and access speed of data should be fast. And there are many situations at where data size is fluctuate and position also. In those conditions data is stored in tree form. Best example for tree is Database design. Different tree structure may used at different situation.
Special data structure called try which is also a type of tree data structure. This is one implementation.
Monitoring the evolution of retinal pathologies can be done by comparing images from the same patient captured at different times. Therefore when a new retinal image is recorded it is important to identify other images in the image database that belong to the same patient. This paper explores the combination of Tree Data Structures with Fractal Dimension image feature descriptors in order to identify a subset of images of the same patient.
Tree Data Structure As explained in the Introduction, a Tree Data Structure may be used to efficiently search large image databases looking for the best matches to a given (key) image. A Tree Data Structure construction organizes training data in such a way that similar descriptors are clustered together at the different tree nodes. The root node of the Tree Data Structure represents all descriptors from all images

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