National Library of Medicine (NLM) at ImageCLEF2015: Medical Clustering Task

Besides recognizing medical image modalities, such as Xrays, MRIs, histology images, fluorescence microscopy images, endoscopy images, photos, illustrations, etc., the detection of visual content is equally important. Once the main modality class is detected, a modality such as Xray can be broken to different sub-classes representing different body parts such as arms, legs, neck, torso, etc. Such a classification can further help the current image-based search engines to return appropriate results based on visual content similarity. For our participation in the ImageCLEF2015 Medical image clustering task, we implemented a classification scheme based on a neural network using two different feature collections – which proved their value in object recognition and chest X-ray analysis.