An Evaluation on the Robustness of Five Popular Keypoint Descriptors to Image Modifications Specific to Laser Scanning Microscopy

Abstract

Laser scanning microscopy (LSM) techniques are of paramount importance at this time for key domains such as biology, medicine, or materials science. Computer vision methods are instrumental for boosting the potential of LSM, providing reliable results for important tasks, such as image segmentation, registration, classification, or retrieval in a fraction of the time that a human expert would require (at similar or even higher accuracy levels). Image keypoint extraction and description represent essential building blocks of modern computer vision approaches, and the development of such techniques has gained massive interest over the past couple of decades. In this paper, we compare side-by-side five popular keypoint description techniques, scale invariant feature transform (SIFT), speeded-up robust features (SURF), binary robust invariant scalable keypoints (BRISK), fast retina keypoint (FREAK) and BLOCK, with respect to their capacity to represent in a reproducible manner image regions contained in LSM data sets acquired under different acquisition conditions. We evaluate this capacity in terms of descriptor matching performance, using data sets acquired in a principled manner and a thorough Precision-Recall analysis. We identify which of the five evaluated techniques is most robust to specific LSM image modifications associated to the laser beam power, photomultiplier gain, or pixel dwell, and show that certain pre-processing steps have the potential to enhance keypoint matching.