An Application Driven Comparison of Several Feature Extraction Algorithms in Bronchoscope Tracking During Navigated Bronchoscopy

Abstract. This paper compares Kanade-Lucas-Tomasi (KLT), speeded up robust feature (SURF), and scale invariant feature transformation (SIFT) features applied to bronchoscope tracking. In our study, we first use KLT, SURF, or SIFT features and epipolar constraints to obtaininterframe translation (up to scale) and orientation displacements and Kalman filtering to recover an estimate for the magnitude of the motion (scale factor determination), and then multiply inter-frame motion parameters onto the previous pose of the bronchoscope camera to achieve the predicted pose, which is used to initialize intensity-based image registration to refine the current pose of the bronchoscope camera. We evaluate the KLT-, SURF-, and SIFT-based bronchoscope camera motion tracking methods on patient datasets. According to experimental results, we may conclude that SIFT features are more robust than KLT and SURF features at predicting the bronchoscope motion, and all methods for predicting the bronchoscope camera motion show a significant performance boost compared to sole intensity-based image registration without an additional position sensor

FPGA Accelerated Discrete-SURF for Real-Time Homography Estimation

This paper describes our hardware accelerated, FPGA implementation of SURF, named Discrete SURF, to support real-time homography estimation for close range aerial navigation. The SURF algorithm provides feature matches between a model and a scene which can be used to find the transformation between the camera and the model. Previous implementations of SURF have partially employed FPGAs to accelerate the feature detection stage of upright only image comparisons. We extend the work of previous implementations by providing an FPGA implementation that allows rotation during image comparisons in order to facilitate aerial navigation. We also expand beyond feature detection as the complete Discrete SURF algorithm is run on the FPGA, rather than piped into processors. This not only minimizes overhead and increases the parallelization of the algorithm, but also allows the algorithm to be easily ported to different FPGAs. Furthermore, the Discrete SURF module is a logic-only implementation that does not rely on external hardware which therefore decreases the overall size, weight and power of the device while also allowing for easy FPGA to ASIC conversion. We evaluate the Discrete SURF algorithm in terms of performance against the original SURF and upright SURF algorithms implemented in OpenCV. Finally, we show how Discrete SURF is more compatible with an aerial navigation scenario than previous works, since rotation invariance must be considered in addition to scale