Open Access

ARTICLE

Anatomical Feature Segmentation of Femur Point Cloud Based on Medical Semantics

by Xiaozhong Chen^*

School of Intelligent Manufacturing, Changzhou Vocational Institute of Engineering, Changzhou, 213164, China

* Corresponding Author: Xiaozhong Chen. Email:

Molecular & Cellular Biomechanics 2023, 20(1), 1-14. https://doi.org/10.32604/mcb.2022.026964

Received 06 October 2022; Accepted 03 January 2023; Issue published 20 June 2023

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.

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Keywords

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