Estimating Deformation of Neurons in Fluorescent Images

Abstract

A main challenge in deformation estimation for fluorescent imaging is to decrease the effects of unavoidable random photon shot noise of fluorescence. To solve this open problem, an efficient second-order minimization (ESM) based non-rigid deformation estimation method on fluorescent imaging of neurons is proposed as a visual aid tool for understanding the relationship of neuron activities and behaviors. Because local features, such as corners, lines, arc segments, usually used for deformation estimation, could be compromised by fluorescence noise, global intensity information of all the pixels in a region of interest (ROI) is used as a texture pattern to guide parameterized deformation estimation. By satisfying this principle, three deformation models including affine, homography and Thin Plate Spline (TPS) based on the ESM algorithm are implemented and evaluated. The experimental results illustrate that the homography is the best choice of correctly and robustly estimating parameters for fluorescent deformations under our experimental condition. By using these parameters, the fluorescent intensity in restored image can be measured and analyzed without the disturbance of noise and deformation which can be significant for understanding how nervous system affects and controls animals' behaviors.