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for on-line monitoring of micro-spheres in an optical tweezers-based assembly cell. ASME Journal of Computing and Information Science in Engineering, 7(4):330-338, 2007. Readers are encouraged to get the official version from the journal's web site or by contacting Dr. S.K. Gupta (skgupta@umd.edu). AbstractOptical tweezers have emerged as a powerful tool for micro and nanomanipulation. Using optical tweezers to perform automated assembly requires on-line monitoring of components in the assembly workspace. This paper presents algorithms for estimating 3-dimensional positions of micro-spheres in the assembly workspace. Algorithms presented in this paper use images obtained by optical section microscopy. The images are first segmented to locate areas of interest and then image gradient information from the areas of interest is used to locate the positions of individual micro-spheres in the XY-plane. Finally, signature curves are computed and utilized to obtain the Z-locations of spheres. We have tested these algorithms with glass micro-spheres of two different sizes under different illumination conditions. Our experiments indicate that the algorithms described in this paper provide sufficient computational speed and accuracy to support the operation of optical tweezers.

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Radiation therapy plays an essential role in the treatment of cancer. In radiation therapy, the ideal radiation doses are delivered to the observed tumor while not affecting neighboring normal tissues. In three-dimensional computed tomography (3D-CT) scans, the contours of tumors and organs-at-risk (OARs) are often manually delineated by radiologists. The task is complicated and time-consuming, and the manually delineated results will be variable from different radiologists. We propose a semi-supervised contour detection algorithm, which firstly uses a few points of region of interest (ROI) as an approximate initialization. Then the data sequences are achieved by the closed polygonal line (CPL) algorithm, where the data sequences consist of the ordered projection indexes and the corresponding initial points. Finally, the smooth lung contour can be obtained, when the data sequences are trained by the backpropagation neural network model (BNNM). We use the private clinical dataset and the public Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) dataset to measure the accuracy of the presented method, respectively. To the private dataset, experimental results on the initial points which are as low as 15% of the manually delineated points show that the Dice coefficient reaches up to 0.95 and the global error is as low as 1.47 × 10. The performance of the proposed algorithm is also better than the cubic spline interpolation (CSI) algorithm. While on the public LIDC-IDRI dataset, our method achieves superior segmentation performance with average Dice of 0.83.

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