Fast quantitative image processing methods and biological image analysis

The cellular neural-nonlinear network (CNN) and the Cellular Wave Computing paradigm was applied to solve a wide range of challenging problems in the last decade. The first problem I have been working on is the real-time three dimensional reconstruction of human heart cavities from ultrasound recordings. Reliable medical diagnosis requires the accurate estimation of cardiac measures like ejection fraction, cavity volume and wall thickening. Currently, medical experts manually measure these indexes prone to subjective bias and error. I developed a computational method providing on-line, automated 3D reconstruction of the right atrium together with the visualization of the atrial septal defect (ASD). I evaluated the accuracy of the method via comparing algorithm results with phantom and clinical echocardiographic data sets manually traced by independent cardiologist experts. Error of the algorithm proved to be comparable to the inter-observer variability between independent experts. Interactive planning of surgical interventions in pediatric cardiology was presented as an illustrative example demonstrating the clinical potential of the method. The second problem I addressed was the quantification of the dendritic morphology of retinal ganglion cells (RGC). A fundamental paradigm of neuroscience is that neural structure is related to neural function. At the cellular level it was shown in many studies that neurons with different shapes have different function. At a higher organizational level it was shown that the dendrites and axonal arborization of neurons in several brain regions are organized in thin strata and neural function varies across strata. A striking example is the mammalian retina where the inner plexiform layer (IPL) consists of about ten different, well-defined strata and each strata extracts a different feature from the visual scene forming a stack of image representations in the retina. In brain regions where different functions are encoded in different strata, a powerful morphological approach would be to quantitatively define the strata which are formed by dendritic and axonal arborization of neurons. Then from simply knowing the depth of stratification of a stained neuron one could deduce its function. I developed an automated algorithm that can scan a large number of cells from a retina and quantify the ramification depth of their dendrites. One candidate mechanism that can play a role in efficient information processing is synchronization. The main problem in analyzing synchronization phenomena in oscillatory networks is that models are non-linear, high dimensional systems. I developed an algorithm that can teach dynamical behavior to a network of oscillators. Using this algorithm, I uncovered two exciting new phenomena. First, I showed examples where switching on coupling between oscillators modified the qualitative behavior of the cells, i.e. proper coupling settings can transform chaotic behavior to periodic or vice versa. Second, I demonstrated for the first time that highly asymmetric interaction weights can synchronize multiple, coexisting cell groups. Despite the asymmetry in the interaction pattern, the spatial layout of the synchronized cell groups followed the topological symmetries of the network.