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학술대회 A SSLBP-based Feature Extraction Framework to Detect Bones from Knee MRI Scans
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저자
문진영, 장유정, 손성호, 윤현중, 김존
발행일
201810
출처
Research in Adaptive and Convergent Systems (RACS) 2018, pp.23-28
DOI
https://dx.doi.org/10.1145/3264746.3264778
협약과제
18ZR1200, 지능형 전파센서 및 무선 에너지 전송 원천기술 개발, 이호진
초록
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.
KSP 제안 키워드
Accuracy Rate, Feature extraction framework, Fuzzy c-means Clustering, Human body, Intensity-based, Knee MRI, Local binary Pattern, Magnetic resonance(MR), Scale space, deep features, feature extraction method