Automatic targeting of plant cells to perform tasks like extraction of chloroplast is often desired in the study of plant biology. Hence, this paper proposes an improved cell segmentation method combined with a robust tracking algorithm for vision-guided micromanipulation in plant cells. The objective of this work is to develop an automatic plant cell detection and localization technique to complete the automated workflow for plant cell manipulation. The complex structural properties of plant cells make both segmentation of cells and visual tracking of the microneedle immensely challenging, unlike single animal cell applications. Thus, an improved version of watershed segmentation with adaptive thresholding is proposed to detect the plant cells without the need for staining of the cells or additional tedious preparations. To manipulate the needle to reach the identified centroid of the cells, tracking of the needle tip is required. Visual and motion information from two data sources namely, template tracking and projected manipulator trajectory are combined using score-based normalized weighted averaging to continuously track the microneedle. The selection of trackers is influenced by their complementary nature as the former and latter are individually robust against physical and visual uncertainties, respectively. Experimental results validate the effectiveness of the proposed method by detecting plant cell centroids accurately, tracking the microneedle constantly and reaching the plant cell of interest despite the presence of visual disturbances.

Percutaneous Nephrolithotomy is a recommended treatment method for large kidney stone removal. However, the first and most important step, i.e., getting the percutaneous access to create the tract between the targeted calyx and the flank skin, is challenging as the surgeon is often occupied by several tasks at a given time. Therefore, in this paper, we propose a robotic assisted system that collaborates with the surgeon and provides assistance in order for the surgeons to focus on more critical jobs resulting in better surgical performance. A procedure for this robot including three working stages is described. This procedure allows the surgeon to choose a suitable percutaneous target using an ultrasound probe based on his or her experience and the robot will track the respiratory motion of the target kidney stone and insert the needle automatically after the surgeon releases the probe. Experiments are conducted to demonstrate the procedure with the proposed assisted robot for PCNL.