Deep learning and SURF for automated classification and detection of calcaneus fractures in CT images

Yoga Dwi Pranata, Kuan Chung Wang, Jia Ching Wang, Irwansyah Idram, Jiing Yih Lai, Jia Wei Liu, I. Hui Hsieh

Research output: Contribution to journalArticlepeer-review

90 Scopus citations


Background and objectives: The calcaneus is the most fracture-prone tarsal bone and injuries to the surrounding tissue are some of the most difficult to treat. Currently there is a lack of consensus on treatment or interpretation of computed tomography (CT) images for calcaneus fractures. This study proposes a novel computer-assisted method for automated classification and detection of fracture locations in calcaneus CT images using a deep learning algorithm. Methods: Two types of Convolutional Neural Network (CNN) architectures with different network depths, a Residual network (ResNet) and a Visual geometry group (VGG), were evaluated and compared for the classification performance of CT scans into fracture and non-fracture categories based on coronal, sagittal, and transverse views. The bone fracture detection algorithm incorporated fracture area matching using the speeded-up robust features (SURF) method, Canny edge detection, and contour tracing. Results: Results showed that ResNet was comparable in accuracy (98%) to the VGG network for bone fracture classification but achieved better performance for involving a deeper neural network architecture. ResNet classification results were used as the input for detecting the location and type of bone fracture using SURF algorithm. Conclusions: Results from real patient fracture data sets demonstrate the feasibility using deep CNN and SURF for computer-aided classification and detection of the location of calcaneus fractures in CT images.

Original languageEnglish
Pages (from-to)27-37
Number of pages11
JournalComputer Methods and Programs in Biomedicine
StatePublished - Apr 2019


  • Calcaneus fracture
  • Computed tomography image
  • Convolutional neural networks
  • Residual network
  • Visual geometry group


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