A Texture based Image Retrieval for Different Stages of Alzheimer's Disease

In the last few years, using digital images have become significant across most of the sectors including healthcare and medical labs. To analyze and interpret the large collection of images having a complex disease pattern requires the knowledge of medical experts. So, the image retrieval technique plays an important role to assist the doctors to carefully examine an image of a new patient by comparing with most similar images existing in the database and also to take a correct decision during diagnosis. So, we carried out our studies by collecting the images of Magnetic Resonance Imaging (MRI) from the Open Access Series of Imaging Studies (OASIS) database. Later, we have categorized the collected MRI images into three different groups based on the size of a ventricular region of the brain and then employed second and higher order statistical methods to extract the textural features from each image. Thus, we obtain multiple textural features using Gray Level Co-occurrence Matrix (GLCM) and Law Texture Energy Measure. After obtaining the textural features, the top matched images are retrieved based on the similarity measure computed between the feature vector of a query image and the images present in the database. Finally, the retrieval performance is compared for the extracted texture features from GLCM, Laws Texture Energy Measure and a combination of these two methods. The combination of features from the above methods shows the better precision of 80% and 60 % in the retrieval of Group1 and Group3 images.