Assessing the Efficiency of the Image Segmentation Algorithms
Damjan Zazula · 2001
A segmentation efficiency of the algorithms for medical images should be measured on a set of clinical data, what is a very demanding task, due to deficiency of golden standards, standardised statistical protocols, appropriate metrics, and tedious data gathering. In this paper, a new procedure for easier evaluation of the segmentation algorithm efficiency is proposed. First, an image is segmented, afterwards, the regions obtained are labelled. The regions described parametrically are then compared to the original objects. A decision about the region recognition is taken considering an intersection between the segmented region and the original object. The algorithm'sefficiency is measured with two statistics: the ratios of correctly recognised objects and regions. The efficiency on an entire image set is determined by averaging the ratios. A final assesment of the segmentation efficiency is determined on a product of both statistics introduced. The efficiency of ten well-known segmentation algorithms is measured according to the proposed evaluation procedure, first, on 50 artificial images where the OPTIMAL algorithm proved to be the best, and afterwards, on 30 real images---the best was UNION-FIND.