Neural network and genetic algorithm based hybrid model for content based mammogram image retrieval

In this study, an approach is described to content-based retrieval of medical images from a database provide a preliminary demonstration of our approach as applied to retrieval of digital mammograms. In the medical-imaging context, the ultimate aim of Content Based Image Retrieval (CBIR) is to provide radiologists with a diagnostic aid in the form of display of relevant past cases, along with proven pathology and other suitable information. We propose a new hybrid approach to content-based image retrieval. Contrary to the single feature vector approach which tries to retrieve similar images in one step, this method uses a two-step approach to retrieval. In the first step, we propose the use of a neural network called Self Organizing Map (SOM) for clustering the images with respect to their basic characteristics. In the second step, the GA based search will be made on a sub set of images which were having some basic characteristics of the input query image. We applied our approach to a database of high resolution mammogram images and show that this method radically improves the retrieval precision over the single feature vector approach. To determine whether our CBIR system is helpful to physicians, we conducted an evaluation trial with five radiologists. The results show that our system using genetic algorithms retrieval doubled the doctors' diagnostic accuracy. Moreover, this method is faster and has higher retrieval accuracy compared to the single stage methods. © 2009 Asian Network for Scientific Information.