Hand initialization and tracking using a modified KLT tracker for a computer vision-based breast self-examination system

Abstract

This paper presents a new algorithm for tracking the hand during palpation in a breast self-examination video capture using a modified KLT feature tracker. This is implemented primarily using Shi-Tomasi corner detection and Lucas-Kanade optical flow. A novel hand initialization technique was developed using Shi-Tomasi corner detection, outlier elimination, ellipse fitting, and target estimation in order to locate specifically the finger pads. Then, continuous hand tracking is achieved using Lucas- Kanade optical flow and a novel evaluation and screening of displacement vectors. A dataset of 14 video sequences was used to test the performance of the proposed algorithm. Experiments revealed efficient tracking capability of the algorithm with an overall F-score of 94.61%.