Abstract—Magnetic Resonance Imaging play a vital role in the decision-diagnosis process of brain MR images. For an accurate diagnosis of brain related problems, the experts mostly compares both T1 and T2 weighted images as the information presented in these two images are complementary. In this paper, rotational and translational invariant form of Local binary Pattern (LBP) with additional gray scale information is used to retrieve similar slices of T1 weighted images from T2 weighted images or vice versa. The incorporation of additional gray scale information on LBP can extract more local texture information. The accuracy of retrieval can be improved by extracting moment features of LBP and reweighting the features based on users’ feedback. Here retrieval is done in a single subject scenario where similar images of a particular subject at a particular level are retrieved, and multiple subjects scenario where relevant images at a particular level across the subjects are retrieved. Keywords—Local Binary pattern (LBP), Modified Local Binary pattern (MOD-LBP), T1 and T2 weighted images, Moment features. I. INTRODUCTION HE advantage of magnetic resonance imaging over other modalities persuade the radiologists to handle various brain related issues. MRI operates at radio-frequency (RF) range; thus there is no ionizing radiation involved. Furthermore, MRI can generate excellent soft tissue contrast, and has the capability of producing images at any orientation. Moreover the information content present in MR images is extremely rich compared to other imaging modalities as it operates at radio-frequency range. The first step in reporting of the magnetic resonance images is reviewing of the cross sectional images at various levels. Generally, the inter-patient search which can compare multiple patients and retrieve relevant cases among them will especially help the expert in diagnosis of structure-specific diseases, such as hippocampus or basal ganglia disorders. The retrieval of T1-weighted and T2-weighted images of a same subject at a particular level from a large data base is also quite useful in the decision-diagnosis process as the information available in the images is complementary. The problem of locating desired slices from a large database is challenging due to Inter- and intra patient intensity variations, intensity non- uniformity, misalignment of images due to head motion etc. The intensity values of T1- weighted and T2-weighted mages Abraham Varghese is with the Adi Shankara Institute of Engineering and Technology, Kalady, Kerala, India (e-mail: abrahamvarghese77@gmail.com). Kannan Balakrishnan is with the Cochin University of Science and Technology, Cochin, Kerala, India. Reji R. Varghese is with the Co-Operative Medical College, Cochin, India. Joseph S. Paul is with the Indian Institute of Information Technology and Management, TVM, Kerala, India. of a particular person at a particular level itself vary drastically. Researchers [1]-[3] considered similar slice retrieval problem on a same class of images by giving importance to similarity of anatomical structures. Unay et al. [4], [5] addressed brain image retrieval problem using Local Binary Patterns (LBP) in which similarity attributes of anatomical structures are extracted using fiducial points which are obtained using a Kanade–Lucas–Tomasi (KLT) transform. In this paper, in order to handle intensity related problems, a local measure called Modified Local Binary Pattern (MOD- LBP) is used which is relevant as MR image is locally smooth. It is obtained by assigning weights to the P-bit binary pattern in LBP, based on the squared difference of individual pixels from the average value. As every pixel in the local neighborhood is involved in the MOD-LBP computation, the method is invariant to some basic geometric transformations and intensity variations locally. In order to achieve reliable image retrieval performance in the presence of global misalignments, the image is converted to polar form (r,θ) by assuming centroid as the origin of the image and thus it avoids registration. Thus features are extracted in such a way that it is invariant to both rotation and translation. The remainder of this paper is organized as follows. Section II describes the methodology. Section III discusses the experimental results. Finally, discussion and conclusion is presented. II. METHODOLOGY The overview of the image retrieval scheme is shown in Fig. 1. The MOD-LBP image code is computed using a circular neighborhood with P pixels and radius R. The histogram of MOD-LBP are extracted spatially and non- spatially [6]. The MOD-LBP image is converted to polar form (r, θ) in order to compensate for rotation and translation in spatial description of the features by taking centroid as the origin of the image. The output image obtained is of size N × N with N points along the r-axis and N points along the θ-axis. The pixel value at the non-integer coordinate of the image is estimated using bilinear interpolation. The histogram of MOD-LBP is computed spatially, where the entries of each bin are indexed over angularly partitioned regions. The pixel intensities are brought in the range [0, L], where L is a positive integer and normalized histogram of the image is taken as feature vectors for similarity computation. The images in the database are ranked based on the Bhattacharya distance between query and database images. The average Abraham Varghese, Kannan Balakrishnan, Reji R. Varghese, and Joseph S. Paul Content Based Image Retrieval of Brain MR Images across Different Classes T World Academy of Science, Engineering and Technology International Journal of Electrical and Computer Engineering Vol:7, No:8, 2013 967 International Scholarly and Scientific Research & Innovation 7(8) 2013 scholar.waset.org/1307-6892/16124 International Science Index, Electrical and Computer Engineering Vol:7, No:8, 2013 waset.org/Publication/16124