Semi-automated annotation of histology images : Development and evaluation of a user friendly toolbox

Abstract: Image segmentation has many areas of application, one of them being in medical science. When segmenting an image, there are many automatic approaches that generally do not let the user change the outcome. This is a problem if the segmentation is badly done. On the other hand there is the manual approach which is slow and cumbersome since it relies heavily on the users effort. This thesis presents a semi-automated approach that allow user interaction and computer assisted segmentation, which was realized in a hi-fi prototype. The prototype made use of SLIC superpixels which the user could combine with interactions to create segments. The prototype was iteratively developed and tested to ensure high usability and user satisfaction. The final prototype was also tested quantitatively to examine if the process of segmenting images had been made more efficient, compared to a manual approach. It was found that the users got a better result in the prototype than the manual if the same time was spent segmenting. Although it was found that the users could not segment images faster by using the prototype than the manual process, it was believed that it could be made more efficient with superpixels that followed the natural border of the image better.