Edge, Ridge, and Blob Detection with Symmetric Molecules
This work addresses feature detection in image processing for applications such as medical imaging and biological analysis, representing an incremental improvement with novel method elements.
The paper tackled the problem of detecting and characterizing edges, ridges, and blobs in 2D images by exploiting symmetry properties in multiscale systems using alpha-molecules, resulting in feature detectors that are noise-stable and contrast-invariant, with accuracy and robustness validated against state-of-the-art algorithms in synthetic and real-world applications like blood vessel detection and cell colony counting.
We present a novel approach to the detection and characterization of edges, ridges, and blobs in two-dimensional images which exploits the symmetry properties of directionally sensitive analyzing functions in multiscale systems that are constructed in the framework of alpha-molecules. The proposed feature detectors are inspired by the notion of phase congruency, stable in the presence of noise, and by definition invariant to changes in contrast. We also show how the behavior of coefficients corresponding to differently scaled and oriented analyzing functions can be used to obtain a comprehensive characterization of the geometry of features in terms of local tangent directions, widths, and heights. The accuracy and robustness of the proposed measures are validated and compared to various state-of-the-art algorithms in extensive numerical experiments in which we consider sets of clean and distorted synthetic images that are associated with reliable ground truths. To further demonstrate the applicability, we show how the proposed ridge measure can be used to detect and characterize blood vessels in digital retinal images and how the proposed blob measure can be applied to automatically count the number of cell colonies in a Petri dish.