Content-based image retrieval in dermatology

Ever since digital imaging became available, researchers have envisioned expert systems with many thousands of images that would help dermatologists make diagnoses, increasing their accuracy and improving patient care. Nowadays such databases are available, but most research so far has only focused on a very small subset of these images, those involving close-ups of brown lesions which are possibly malignant melanomas. This thesis instead explores mostly other types of images from dermatology, using images provided by the University Medical Center Groningen. Using and extending techniques from image processing and machine learning, a content-based image retrieval system was created that can effectively retrieve lesions that are similar to those in a query image. Features related to the color and boundary sharpness of a lesion were found to perform best, while features related to fractalness are ineffective. Using the best performing features the system can on average retrieve three images with the correct diagnosis in the first ten results, and can be used to give a correct automated diagnoses for about half of these new queries. It is concluded that automated CBIR systems can help to make better use of large dermatological databases and effectively assist in making dermatological diagnoses.