Non – Linear Cellular Automata In Improving The Quality Of Clustering For Medical Image Processing

Clustering can be applied to medical image processing on the grounds of its potential improved effectiveness over conventional image search. Clustering is a mostly unsupervised procedure and the majority of the clustering algorithms depend on certain assumptions in order to define the subgroups present in a data set .A clustering quality measure is a function that, given a data set and its partition into clusters, returns a non-negative real number representing the quality of that clustering. Moreover, they may behave in a different way depending on the features of the data set and the input parameters values. Therefore, in most applications the resulting clustering scheme requires some sort of evaluation as regards its validity. The quality of clustering can be enhanced by using a Cellular Automata Classifier for medical image processing. In this paper we take the view that if cellular automata with clustering is applied to search results, then it has the potential to increase the retrieval effectiveness compared both to that of static clustering and of conventional image search. We conducted a number of experiments using five image collections and four hierarchic clustering methods. Our results show that the effectiveness of query-specific clustering with cellular automata is indeed higher, and suggest that there is scope for its application to medical image processing.