Retrieval of biomedical pictures is a significant side of computer based diagnosis. It helps the radiologist and restorative authority to spot and analyze the particular disease. This paper proposes a Content Based Medical Image Retrieval (CBMIR) approach for retrieving similar biomedical images. The extraction of retrieving features is based on histogram of oriented gradients (HOG) and canny edge detection. To reduce the dimensionality, principal component analysis(PCA) is employed over the feature vector. The experiments are conducted on high-resolution computed tomography medical images of lungs. With the average retrieval rate (ARR) and average retrieval precision (ARP), the performance of the proposed approach is analyzed and compared with other existing methods viz. Local Binary Pattern (LBP), LBP with uniform patterns (LBPu2), Local Mesh Pattern with uniform patterns (LMePu2) and LMeP with gabor transform (GLMeP).

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