A SSLBP-based feature extraction framework to detect bones from knee MRI scans

The medical industry is currently working on a fully autonomous surgical system, which is considered a novel modality to go beyond technical limitations of conventional surgery. In order to apply an autonomous surgical system to knees, one of the primarily responsible areas for supporting the total weight of human body, accurate segmentation of bones from knee Magnetic Resonance Imaging (MRI) scans plays a crucial role. In this paper, we propose employing the Scale Space Local Binary Pattern (SSLBP) feature extraction, a variant of local binary pattern extractions, for detecting bones from knee images. The experimental results demonstrate that the proposed method has an average accuracy rate of 96.10% with an average MCC rate of 88.26%, which significantly outperforms existing intensity-based methods such as fuzzy c-means clustering and deep feature extraction method.