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Local Diagonal Extrema Pattern A New and Efficient Feature Descriptor for CT Image Retrieval - 2015

PROJECT TITLE:

Local Diagonal Extrema Pattern A New and Efficient Feature Descriptor for CT Image Retrieval - 2015

ABSTRACT:

The medical image retrieval plays an vital role in medical diagnosis where a physician can retrieve most similar pictures from template images against a query image of a particular patient. During this letter, a brand new and efficient image features descriptor based mostly on the local diagonal extrema pattern (LDEP) is proposed for CT image retrieval. The proposed approach finds the values and indexes of the native diagonal extremas to exploit the relationship among the diagonal neighbors of any center pixel of the image using initial-order native diagonal derivatives. The intensity values of the native diagonal extremas are compared with the intensity value of the middle pixel to utilize the relationship of central pixel with its neighbors. Finally, the descriptor is formed on the premise of the indexes and comparison of center pixel and native diagonal extremas. The consideration of solely diagonal neighbors greatly reduces the dimension of the feature vector which hurries up the image retrieval task and solves the “Curse of dimensionality” problem conjointly. The LDEP is tested for CT image retrieval over Emphysema-CT and NEMA-CT databases and compared with the prevailing approaches. The superiority in terms of performance and efficiency in terms of speedup of the proposed methodology are confirmed by the experiments.


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