Thesis - Open Access
Master of Science (MS)
Electrical Engineering and Computer Science
Sung Y. Shin
classification, feature extraction, image segmentation, Local Binary Pattern, Magnetic Resource Imaging, Support Vector Machine
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 proposed methods consist of two phases. In the first phase, training phase, the SSLBP feature is defined and extracted to obtain the characteristic of knee bone texture problem. And based on the extracted feature from the training dataset, Support Vector Machine (SVM) structure is generated for classifying. The second phase is segmentation phase. The knee MRI is preprocessed to remove noise, and the pre-processed image is classified based on the feature extraction. Finally, in the segmentation phase, the classified image is post-processed by using fuzzy c-means clustering technique. The experimental results demonstrate that the proposed method has an average accuracy rate of 96.10% with an average Matthews Correlation Coefficient (MCC) rate of 88.26%, which significantly outperforms existing intensity-based methods such as fuzzy c-means clustering and deep feature extraction method.
Library of Congress Subject Headings
Knee -- Magnetic resonance imaging.
Knee -- Surgery.
Includes bibliographical references
Number of Pages
South Dakota State University
In Copyright - Non-Commercial Use Permitted
Mun, Jinyeong, "A Scale Space Local Binary Pattern (SSLBP) – Based Feature Extraction Framework to Detect Bones from Knee MRI Scans" (2018). Electronic Theses and Dissertations. 2951.