Abstract:
Image Retrieval is one of the most applicable image processing technique, which has been used extensively. Feature extraction is one of the most important procedures used for interpretation and indexing images in content - based image retrieval systems. Effective storage, indexing and managing a large number of image collections is a critical challenge in computer systems. There are many proposed methods to overcome these problems. However, the rate of accurate image retrieval and speed of retrieval is still an interesting field of research. In this study, we propose a new method based on texture based image retrieval system. It is particularly valuable in Histology image analysis for interpreting varying tissue composition and architecture into histological concepts. The proposed method consists of three steps. Texture features are retrieved using integer Wavelet, The retrieval is performed using a combination of simple and weighted ( class membership based ) distance metric in complete search space unlike the conventional classifier based retrieval techniques. The proposed technique also provides flexibility in reducing the search space in steps increasing the speed of retrieval at the cost of gradual reduction in accuracy. Experiments were performed on two different collections of histology images. The performance of the method is evaluated using three texture data sets varying in orientations, complexity and number of classes. Average normalized rank and combination of precision and recall are considered as metrics to evaluate and compare the proposed method against different methods.
Keywords: Image retrieval, fuzzy logic, integer wavelet, medical image retrieval, histology.