The future of biomedical image analysis is no longer in anatomical imaging but in imaging of pathways and mechanisms at the cellular level and below. In this talk I present seven years of development of image analysis techniques for rolling leukocytes observed in vivo. Rolling leukocytes are activated white blood cells. The motion, shape, flux, number and position of these cells are important indicators of the inflammatory process. Measuring image-derived parameters are vital to validating anti-inflammatory drugs and to understanding the basic mechanism of inflammatory diseases such as atherosclerosis and arthritis. To date, these image features are typically derived manually due to the difficulty associated with intravital image clutter, noise, occlusion, instability, poor contrast, contrast changes and shape deformation.
First, I will discuss diffusion-based image enhancement methods appropriate to improve these types of images, automated registration techniques that allow leukocyte detection and tracking in a moving field of view. The second portion of the talk focuses on novel cell detection methods for intravital microscopy. The methods include a level set solution, the gradient inverse coefficient of variation (GICOV) technique, and the more recent Poisson inverse gradient approach. The third part of the talk details tracking methods used for rolling leukocytes. These are divided into two categories: active contour approaches and particle filter approaches. For both detection and tracking, real video data examples show the efficacy of the developed techniques.
New directions in cellular image analysis are discussed including high content screening, collaborative hardware-software co-design, and future work in image analysis for systems biology.