||This master thesis develops a novel system to model the tubular structure
in thin sections of testicular tissue and count the Sertoli cells. A three-phase method is proposed to model the tubular structure in microscopic images of the tissue, the model is deployed to detect the cells.
In the first phase, the germ-mass, which represents the inside layer of tubules, are detected. All cells are detected by radial symmetry transform and then the graph cut algorithm is used to separate the germ cells. Each region covered by a compact set of germ cells is considered as the germ-mass.
In the second phase, all bright areas in the image are detected and used to adjust the germ-mass regions. In the last phase, all edges that are line-like are identified and straight lines are fitted to the edges. The lines are later connected to compensate for the broken parts of the tubules' boundaries.
The closest cells to the germ-mass are chosen as the Sertoli cell candidates.
The approximate boundary of tubules and the angle between the candidate cells are used to detect the Sertoli cells. Our experimental results show that our system is able to detect the tubule and the Sertoli cells with reasonable accuracy. If the method can not find enough edges to approximate the tubule's boundary, detecting Sertoli cells is complicated; the system can report those situations to the experts.
Since we use the symmetry attribute of the cells to detect them, the method is quite robust against noise, artifacts, and non-uniform illumination. The method is able to capture all tubules, even tubules that do not have any bright region in the middle (lumen). To the best of my knowledge, no one has proposed a method to model tubular structure without lumen. The border approximation method can work well even for tubules that are partially in the image. It should be mentioned that the proposed method could be applied to model any tubular structure with one or more cells types.