An Integrated Clustering Method for Medical Images

Medical images became a huge problem due the fast growing size of the medical image repositories, thousands of medical images are produced daily. Medical images repositories need to be well organized using an efficient and fast tool to allow researches or medical experts to extract useful information from it in the right time and as fast as possible. Organizing large medical images repositories helps in many fields as in medical fields that can be useful in diagnosis and knowing the history of a patient and in the researching area as it can be mined easily and be a necessary step before many application as content based image retrieval and medical image classification application. The objective of this paper is to implement a new efficient clustering method for medical images. The system contains three main models, the first is to extract features using gray-level co-occurrence matrix and apply PCA for dimensionality reduction, and then k-means clustering is applied. This second model where the 2D wavelet transforms is applied as a feature extraction and feature selection is used to select most efficient attributes, then k-means clustering is applied. The final and proposed method is to combine the two methods and apply k-means clustering.