Motion Artifact Prediction in Laser Speckle Contrast Imaging : Optical Flow-Based Algorithm for Real-Time Motion Artifact Prediction in Laser Speckle Contrast Imaging

Sammanfattning

Laser Speckle Contrast Imaging (LSCI) is an imaging modality for real-time visualization of microcirculatory blood flow, commonly referred to as perfusion. The technology works by illuminating biological tissue with a laser and capturing the backscattered speckle pattern. The perfusion estimate is based on quantifying the image blurring that occurs due to the speckle pattern fluctuation. During captures, the imaged object has to be almost completely still, or there will be motion artifacts of overestimated perfusion data. PeriCam MultiFlow is a camera system based on LSCI technology that suffers from this limitation. In this thesis, we present an algorithm based on Shi-Tomasi corner detection and sparse Lucas-Kanade optical flow motion estimation, to predict in real-time whether motion artifacts are present in the perfusion images produced by MultiFlow. We evaluate the algorithm’s predictive performance as a binary classification problem using Matthew’s Correlation Coefficient (MCC) metric. We also assess the real-time capability by performing run-time measurements of the algorithm in isolation and when integrated into MultiFlow. We demonstrate that the algorithm performs better in certain cases, particularly on larger motions, while the algorithm struggles to differentiate between small motions and temporal noise. There are also performance differences between imaged objects. For instance, the algorithm is better at making predictions from small motions of a posterior hand than an anterior forearm or foot sole. However, the algorithm’s predictive performance can be consistently decent (MCC above 0.5) by choosing a balanced parameter configuration. Furthermore, we demonstrate that the algorithm is efficient enough for real-time processing in MultiFlow without impairing frame throughput from the camera.