Automatic Detection of Morphologically Distinct Objects in Biomedical Images Using Second Generation Wavelets and Multiple Marked Point Process

摘要

Automatically analyzing morphology of biological objects such as cells, nuclei, and vessels is important for medicine and biology. However, detecting individual biological objects is challenging because biomedical images tend to have a complex structure composed of many morphologically distinct objects and unclear object boundaries. In this paper, we present a novel approach to automatically detect individual objects in biomedical images using a multiple marked point process, in which points are the positions of the objects and marks are their geometric attributes. With this model, we can consider both prior knowledge of the structure of the objects and observed data of an image in object detection. Our proposed method also uses the second generation wavelets-based edge-preserving image smoothing technique to cope with unclear boundaries of biological objects. The experimental results show the effectiveness of our method.