801. Real-Time Automated Segmentation and Classification of Calcaneal Fractures in CT Images
- Author
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Wahyu Rahmaniar and Wen June Wang
- Subjects
Computer science ,lcsh:Technology ,030218 nuclear medicine & medical imaging ,calcaneus ,lcsh:Chemistry ,03 medical and health sciences ,0302 clinical medicine ,Calcaneal fracture ,Anatomical plane ,medicine ,General Materials Science ,Segmentation ,Instrumentation ,lcsh:QH301-705.5 ,Fluid Flow and Transfer Processes ,030222 orthopedics ,Standard test image ,business.industry ,Orientation (computer vision) ,lcsh:T ,Process Chemistry and Technology ,segmentation ,General Engineering ,Pattern recognition ,medicine.disease ,musculoskeletal system ,Sagittal plane ,lcsh:QC1-999 ,Computer Science Applications ,medicine.anatomical_structure ,bone fracture ,lcsh:Biology (General) ,lcsh:QD1-999 ,CT image ,lcsh:TA1-2040 ,Coronal plane ,Artificial intelligence ,Calcaneus ,business ,biomedical imaging ,lcsh:Engineering (General). Civil engineering (General) ,lcsh:Physics - Abstract
Calcaneus fractures often occur because of accidents during exercise or activities. In general, the detection of the calcaneus fracture is still carried out manually through CT image observation, and as a result, there is a lack of precision in the analysis. This paper proposes a computer-aid method for the calcaneal fracture detection to acquire a faster and more detailed observation. First, the anatomical plane orientation of the tarsal bone in the input image is selected to determine the location of the calcaneus. Then, several fragments of the calcaneus image are detected and marked by color segmentation. The Sanders system is used to classify fractures in transverse and coronal images into four types, based on the number of fragments. In the sagittal image, fractures are classified into three types based on the involvement of the fracture area. The experimental results show that the proposed method achieves a high precision rate of 86%, with a fast computational performance of 133 frames per second (fps), used to analyze the severity of injury to the calcaneus. The results in the test image are validated based on the assessment and evaluation carried out by the physician on the reference datasets.
- Published
- 2019