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This paper proposes an automatic error correction method for melanosome tracking. Melanosomes in intracellular images are currently tracked manually when investigating diseases, and an automatic tracking method is desirable. We detect all melanosome candidates by SIFT with 2 different parameters. Of course, the SIFT also detects non-melanosomes. Therefore, we use the 4-valued difference image (4-VDimage) to eliminate non-melanosome candidates. After tracking melanosome, we re-track the melanosome with low confidence again from t + 1 to t. If the results from t to t + 1 and from t + 1 to t are different, we judge that initial tracking result is a failure, the melanosome is eliminated as a candidate and re-tracking is carried out. Experiments demonstrate that our method can correct the error and improves the accuracy.

This paper proposes a melanosome tracking method using Bayes theorem and estimation of movable region of melanosome candidates. Melanosomes in intracellular images are tracked manually now to investigate the cause of disease, and automatic tracking method is desired. Since there are little automatic recognition methods for intracellular images, we can not know which features and classifiers are effective for them. Thus, we try to develop the melanosome tracking using Bayes theorem of melanosome candidates detected by Scale-Invariant Feature Transform (SIFT). However, SIFT can not detect the center of melanosome because melanosome is too small in images. Therefore, SIFT detector is adopted after image size is enlarged by Lanczos resampling. However, there are still many melanosome candidates. Thus, we estimate the movable region of the target melanosome in next frame and eliminate melanosome candidates. After the posterior probability of each candidate is computed by Bayes theorem, and the melanosome with the maximum probability is tracked. Experimental results using the melanosome images of normal and Griscelli syndrome show the effectiveness of our method.