Circular and oval-like objects are very common in cell and micro biology. These objects need to be analyzed, and to
that end, digitized images from the microscope are used so as to come to an automated analysis pipeline. It is essential
to detect all the objects in an image as well as to extract the exact contour of each individual object. In this manner it
becomes possible to perform measurements on these objects, i.e. shape and texture features. Our measurement objective
is achieved by probing contour detection through dynamic programming. In this paper we describe a method that uses
Hough transform and two minimal path algorithms to detect contours of (ovoid-like) objects. These algorithms are based
on an existing grey-weighted distance transform and a new algorithm to extract the circular shortest path in an image. The
methods are tested on an artificial dataset of a 1000 images, with an F1-score of 0.972. In a case study with yeast cells,
contours from our methods were compared with another solution using Pratt’s figure of merit. Results indicate that our
methods were more precise based on a comparison with a ground-truth dataset. As far as yeast cells are concerned, the
segmentation and measurement results enable, in future work, to retrieve information from different developmental stages
of the cell using complex features.
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