FAST AND ACCURATE FEATURE DETECTION AND TRIANGULATION USING TOTAL VARIATION FILTERING OF BIOLOGICAL IMAGES

We consider here the problem of detecting and modeling the essential features present in a biological image and the construction of a compact representation for them which is suitable for numerical computation. The solution we propose employs a variational energy minimization formulation to extract noise and texture, producing a clean image containing the geometric features of interest. Such image decomposition is essential to reduce the image complexity for further processing. We are particularly motivated by the image registration problem where the goal is to align matching features in a pair of images. A combination of algorithms from combinatorial optimization and computational geometry render fast solutions at interactive or near interactive rates. We demonstrate our technique in microscopy images. We are able, for example, to process large, 2048times2048 pixels, histology mouse brain images under a minute creating a faithful and sparse triangulation model for it having only 1.8% of its original pixel count. Models for 512times512 images are typically generated in less than 5 seconds with similar reduced vertex count. These results suggest the relevance of our approach for modeling biomedical images