Image-based area estimation of any connected region using y-convex region decomposition

In this paper, we address the issue of finding the area of any connected region from its digital image. We have reviewed the existing techniques and brought out their shortcomings. In particular, we observe that none of the existing techniques can satisfactorily find the area of certain types of regions. To overcome such shortcomings, we propose an algorithm for decomposing any connected region R into one or more disjoint regions Yi. The decomposition is such that all the sub-regions satisfy a convexity property, referred to as y-convex property. y-Convex region is one in which no vertical line intersects the boundary curve more than twice. An interesting property of a y-convex region is that its contour can be split into an upper curve and a lower curve, and hence known numerical methods can be used to estimate its area. So, our decomposition algorithm enables us to find the area of R as the summation of the areas of all Yi. Since there are no bench mark images for validation, we have created a new database of images including the images used by previous works. Experimental results show that our method has an average accuracy of 99.01% and the paired t-test confirms that the difference between the actual and estimated area is considered to be not quite statistically significant with t and two-tailed p values of 1.68 and 0.0953, respectively. The proposed approach can be potentially used in applications such as geographical survey, morphological analysis of micro-organisms, medical image analysis, crime scene documentation, agriculture, and food industry.