# Real-Time Automated Segmentation and Classification of Calcaneal Fractures in CT Images
## 1 Introduction
This method classifies the calcaneal fractures into two classes: Fractured and non-fractured. However, the classification results do not provide more detailed conclusions about the fractures type of each anatomical plane orientation, where the identification of fracture alone is not sufficient to assess injury severity. Moreover, the orientation of the calcaneus anatomical plane is manually registered and the computation time is 5 min and 10 s, which are not fast enough for real-time applications. In this paper, a new method for the automated segmentation and detection of calcaneal fractures for real-time applications is proposed. The main objective is to provide results for calcaneus segmentation and detection of fractures for each anatomical plane orientation, so that physicians can better, and further assess fractures in the calcaneus region with a shorter processing time. One of the challenges in detecting calcaneal fractures is the different shape of the calcaneus in each patient's CT image [10; 11; 12]. As such, the shape approach cannot be used to find the calcaneus based on its shape in the tarsal bone. Furthermore, due to limited features and colors in the CT images, it is a difficult task to identify calcaneus in the tarsal bone. To solve this problem, the region of interest (ROI) of the calcaneus is determined based on the anatomical plane orientation of the tarsal bones, i.e., transverse, coronal, and sagittal. In addition, due to quality limitations in CT images, it is a challenging task to classify the type of fracture for calcaneus. In particular, mild and small fractures, that only appear slightly in CT images, cannot be detected a physician at first inspection because the physician may not be able to reliably call them to rely on these fractures, due to the quality of CT and the amount of data to be processed [14; 15]. Thus, for fractures that appear slightly in CT images, repeated inspections are required to identify the existence and details of the fracture. Computer-based analyses can be used to process detailed information from several neighboring slices to give instructions to the physician on whether a particular slice contains a fracture, then details of the separation between pieces can be identified [12; 14] can be extracted. However, due to the anatomical variability between individuals, the accuracy in segmentation and detection of fractures in the calcaneus structure remain a challenging task. This proposed method provides the calcaneus segmentation to show the details of the fracturesin the calcaneus region, which can be fragments or lines. This method has performed the segmentation by detecting the calcaneus bone structure, based on the white bone area on the CT images. Then, the fracture type is classified based on the amount of the fragments and the location of the lines fractures. The remainder of this paper is organized as follows. Section 2 introduces a literature review of the classification of calcaneal fractures and describes the main algorithm. Section 3 illustrates the performance of fracture detection results in CT images. Finally, conclusions are drawn in Section 4.