A SSLBP-based feature extraction framework to detect bones from knee MRI scans
| Autor: | Seong-Ho Son, Jin Yeong Mun, John Kim, Hyeun Joong Yoon, Youjeong Jang |
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| Rok vydání: | 2018 |
| Předmět: |
Computer science
business.industry Local binary patterns Feature extraction Image processing Pattern recognition 02 engineering and technology Image segmentation 030218 nuclear medicine & medical imaging Scale space Support vector machine 03 medical and health sciences 0302 clinical medicine 020204 information systems 0202 electrical engineering electronic engineering information engineering Segmentation Artificial intelligence business Cluster analysis |
| Zdroj: | RACS |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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