Local bit plane adjacent neighborhood dissimilarity pattern for medical CT image retrieval

摘要

In this article, new feature descriptor local bit plane adjacent neighborhood dissimilarity pattern (LBPANDP) is introduced for CT image retrieval. In the proposed method, the input image is first decomposed into eight binary bit planes. Then in each of the first four most significant bit planes, for each center pixel, the dissimilarity information of its binary neighbors are encoded into a single value. The encoded values of each bit plane is then compared with the intensity of the center pixel and this relationship is finally encoded to devise the LBPANDP descriptor. The proposed descriptor is highly discriminative and considers only the first four most significant bit planes for encoding, which greatly reduces the dimension of the feature vector. Retrieval performance of LBPANDP is investigated on two CT image databases and the results show a significant improvement in retrieval efficiency by LBPANDPover many recent techniques.