Overview of the ImageCLEF 2015 medical clustering task
M. Ashraful Amin, Mahmood Kazi Mohammed · CLEF (Working Notes) · 2015
There are thousands of unlabeled x-ray images available and in the third world countries more is generated every day. With the advancement of digital technology now a day's digital x-ray imaging techniques are available, however due to the high cost of the machines it is not popular in the third world countries. Moreover, old school x-ray plates are still there. The medical cluster- ing task of ImageCLEF 2015 addresses the issue of automated organization of x-ray images. The challenge is that there are x-ray images containing different parts of human body and the participant have to device a mechanism to identify that body part. The main challenge is that an image could contain several body part and the classifier has to identify all of them separately or as many as possi- ble. Body parts are divided in to four major larger groups: head-neck, upper- limb, body, and lower-limb. The secondary goal of this task is farther partition- ing the initial clusters into sub-clusters, for example the upper-limb cluster can be farther divided into: Clavicle, Scapula, Humerus, Radius, Ulna, and Hand. However, due to the time constrain and difficulty level of the task this year we decided to go with the primary objective. Data was collected by 71 groups from all around the world, however 8 groups submitted the final test results and working note papers were submitted by 6. Interestingly, this 6 groups explored the discriminating ability of 27 different types of feature extraction method and also many different types of classifiers were used. Three different performance measurement is used. Best result for exact match was 0.752; for any match was 0.864; and for Hamming similarity was 0.895.