Patch clamping is used in electrophysiology to study single or multiple ion channels in cells. Multiple micropipettes are used as electrodes to collect data from several cells. Placement of these electrodes is a time consuming and complicated task due to the lack of depth perception, limited view through the microscope lens and the possibility of collisions between micro-pipettes. To aid in this process, a computer-assisted approach is developed using image processing techniques applied to images obtained through the microscope. Image processing algorithms are applied to perform autofocusing, relative depth estimation, distance estimation and tracking of the micro-pipettes in the images without making any major changes in the existing patch clamp equipment. An autofocusing algorithm with a micrometer precision is developed and the relative depth estimation is performed based on autofocusing. A micro-pipette tip detection algorithm is developed which can be used to initialize or reset the tracking algorithm and to calibrate the system by registering the relative image and micro-manipulator coordinates. An image-based tracking algorithm is also developed to track a micro-pipette tip in real time. The real-time tracking data is then used for visual servoing the micro-pipette tips and updating the calibration information.