@Article{mcb.2022.026964,
AUTHOR = {Xiaozhong Chen},
TITLE = {Anatomical Feature Segmentation of Femur Point Cloud Based on Medical Semantics},
JOURNAL = {Molecular \& Cellular Biomechanics},
VOLUME = {20},
YEAR = {2023},
NUMBER = {1},
PAGES = {1--14},
URL = {http://www.techscience.com/mcb/v20n1/53168},
ISSN = {1556-5300},
ABSTRACT = {Feature segmentation is an essential phase for geometric modeling and shape processing in anatomical study of
human skeleton and clinical digital treatment of orthopedics. Due to various degrees of freedom of bone surface,
the existing segmentation algorithms can hardly meet specific medical need. To address this, a novel segmentation
methodology for anatomical features of femur model based on medical semantics is put forward. First, anatomical
reference objects (ARO) are created to represent typical characteristics of femur anatomy by 3D point fitting in
combination with medical priori knowledge. Then, local point clouds between adjacent anatomies are selected
according to the AROs to extract boundary feature point (BFP)s. Finally, the complete model of femur is divided
into anatomical regions by executing the enhanced watershed algorithm guided with BFPs. Experimental results
show that the proposed method has the advantages of automatic segmentation of femoral head, neck and other
complex areas, and the segmentation results have better medical semantics. In addition, the slight modification of
segmentation results can be achieved by adjusting a few threshold parameter values, which improves the convenience of modification for ordinary users.},
DOI = {10.32604/mcb.2022.026964}
}