Semi-automatic segmentation of human anatomical imagery

Abstract

Manual segmentation of anatomical imagery is a challenging and laborious task which this dissertation attempts to alleviate. We present a semi-automatic segmentation system which operates on a new data set of photographic human anatomical imagery. A morphological tree-based segmentation method was utilised in order to reach this aim. We placed a particular focus on elongated structures in order to demonstrate the e ectiveness of the algorithms. The resultant outputs were presented to academics in the anatomical sciences for evaluation. Qualitative and quantitative results which were collected throughout the course of the experimentation phase indicate that the system was successful in producing meaningful labelled segmentation outputs with particularly good performance on elongation, which were commended by the experts. We believe that these results provide a good initialisation step for more re ned labelled images which can be used in a number of di erent professional and educational tools. Furthermore, the outcome of this dissertation demonstrates that a technical window exists in this area, and a foundation for further research has been created in this work.