Novel method for joining missing line fragments for medical image analysis

We present a method of predictive reconstructing connections between parts of object outlines in images. The method was developed mainly to analyze microscopic medical images but is applicable to other types of images. Examined objects in such images are highly transparent, moreover close objects can overlap each other. Thus, segmentation and separation of such objects can be difficult. Another frequently occurring problem is partial blur due to high image magnification. Large focal length of a microscope dramatically narrows the range of sharp image (depth of field). The method is based on edge detection to extract object contours and represent them in a vector form. The logic behind the presented method refers to the Gestalt Laws describing human perception. The method, according to the law of good continuation and the principle of similarity, evaluates the neighborhood the interrupted contour path, and then tries to determine the most appropriate connection with the other parts of the contour. To assess the similarity of contour parts, the method examines the orientation of the line determined by the gradient of the edge, the characteristics of the edge cross section and the direction of its current course. In order to reduce the amount of data and accelerate the method, fragments of detected outlines are represented by vectors in the form of a graph. Thus, the method has faster access to the information on the course of the edges than in the case of bitmap-based representations.