Medical image tracking of fluorescent cells using Otsu model

A computer-aided diagnosis [CAD] system are proposed to segmenting and tracking of fluorescent cells in time-lapse series. We proposed tracking scheme involves two steps. First, coherence-enhancing diffusion filtering is applied on each frame to reduce the amount of noise. Second, the cell boundaries are detected by minimizing the Otsu model in the fast level setlike frameworks, while obtaining smooth contours of foreground objects. Otsu model behaves well in segmenting images of low signal-to-noise ratio. But it gives satisfactory results only when the numbers of pixels in each class are close to each other. Otherwise, it gives the improper results. Experimental results show that the proposed method performs better than the traditional Otsu model for our renal biopsy samples. Finally the tracked cells are demonstrated on time-lapse series of prostate cancer cells. The system, which was tested using a variety of cells, achieved tracking cell images is both fast and robust.