Local Diagonal Extrema Pattern for CT Image Retrieval

The medical image retrieval plays an important role in medical diagnosis where a physician can retrieve most similar images from template imagesagainst a query image of a particular patient. In this letter, a new and efficient image features descriptor based on the local diagonal extrema pattern (LDEP) is proposed for CT image retrieval. The proposed approach finds the values and indexes of the local diagonal extremas to exploit the relationship among the diagonal neighbors of any center pixel of the image using first-order local diagonal derivatives.

The intensity values of the local diagonal extremas are compared with the intensity value of the center pixel to utilize the relationship of central pixel with its neighbors. Finally, the descriptor is formed on the basis of the indexes and comparison of center pixel and local diagonal extremas. The consideration of only diagonal neighbors greatly reduces the dimension of the feature vector which speeds up the image retrieval task and solves the “Curse of dimensionality” problem also. The LDEP is tested for CTimage retrieval over Emphysema-CT and NEMA-CT databases and compared with the existing approaches. The superiority in terms of performance and efficiency in terms of speedup of the proposed method are confirmed by the experiments