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A new feature descriptor called local bit plane-based dissimilarities and adder pattern (LBPDAP) is proposed in this paper for content-based computed tomography (CT) image retrieval. To compute the descriptor, the bit planes of the input image are first extracted. For each pixel of an image, these bit planes are then locally encoded using an adder which combines the center-neighbor dissimilarity information and the neighbor–neighbor mutual dissimilarity information in each bit plane. The encoded bit plane values corresponding to each center pixel are finally compared with the intensity of the center pixel to compute the proposed LBPDAP. In order to limit the feature dimensions, we have considered only four most significant bit planes for LBPDAP computations as the higher bit planes contain more significant visual texture information. The proposed descriptor is low dimensional and experimental results on widely accepted NEMA and TCIA-CT image databases demonstrate better retrieval efficiency of LBPDAP over many recent local pattern-based approaches.