CYU_IM@ImageCLEF 2007: Medical image annotation task
Pei-Cheng Cheng, Wei‐Pang Yang · CLEF (Working Notes) · 2007
The ImageCLEF 2007 Medical Automatic Annotation Task, base on the IRMA project a database of 11,000 fully classified radiographs was used to train a classification system and 1,000 radiographs have to be classified. Radiography medical image always contain particular anatomic regions (lung, liver, head, and so on). Thus, similar images have similar spatial structures. We proposed a relative vector representation that represents the local spatial relationship between pixels. In this experiment, we transform the gray value to relative vector which is an illumination invariant feature. We calculate the occurrence frequency and standard deviation of relative vector as the image feature. Based on the image feature we can find the similar image with less distance. Finally, we use the nearest neighbor method to classify the 1000 test images. In this task, we have submitted one run to medical annotation task. 1,000 radiographs for participants have to be classified. The score of our result is 79.303 which is the error count. The rank of our result is 33 of all 68 runs. The best score around all runs is 26.847 and the worst score of rank 68 is 505.618. The image feature we proposed is easy to implement and the performance of our method is good.