# Real-Time Automated Segmentation and Classification of Calcaneal Fractures in CT Images
## 1 Introduction  
The calcaneus is the largest tarsal bone that has the responsibility of supporting the axial load of the body's weight [1]. A calcaneal fracture is the most common in the tarsal bone fractures, most of which are intra-articular fractures, which usually occur as a result of falling from a height, sports injuries, and vehicle accident [2]. The severity of fracture displacement and the extent of soft tissue injury are directly related to the amount of energy absorbed by the limbs in producing injury [3]. Injuries with higher energies produces soft tissue disorders that are more severe and may cause open fractures [4]. Bleeding fractures into the plane of the facial, which surrounds the heel, produce severe pain above the fracture and may result in a leg compartment syndrome [5]. Furthermore, calcaneal fractures have presented a significant challenge for orthopedic surgeons in patients' treatment [6; 7]. So, detection of the calcaneal fracture is an important subject for patient diagnostic decisions and treatment planning [8; 9]. Modern calcaneal fracture classification systems rely on three-dimensional computed tomography (CT) rather than two-dimensional conventional radiography [10]. Although a CT image contains a significant amount of medical information, it is not accurate enough to examine fractures through manual visual inspection due to its low resolution [11]. Thus, details, such as the skeletal structures, the boundary between internal organs and bone, and soft tissue in the bone may not be accurately seen and assessed depending on the experience of the physician [12]. Therefore, a computer-aided method for fracture detection is needed which can significantly assist physicians to examine CT images,and it is crucial for physicians to make diagnostic decisions, as well as plan treatments, based on this information [13]. In addition, with a computer-aid, faster and more detailed fracture detection can be achieved. Recently, several approaches have been proposed to detect bone fractures in CT images of various areas of the human body. [PERSON] et al. [14] proposed a method for automatic fracture detection of CT images of traumatic pelvic injuries. These fractures are detected using a segmentation technique, which consists of four main parts: Pre-processing, edge detection, shape matching, and Registered Active Shape Model (RASM) with an automatic initialization. However, this segmentation method only applies to the reference frame, which was generated based on previously known pelvic bone anatomy information. [PERSON] et al. [15] implemented a method for the automatic detection of posterior element fractures on spinal CT images. This method used the multi-atlas label fusion to segment the spinal vertebral body and computed the edge map of its posterior elements. But, this segmentation region is predicted on the set of probabilities for fractures along the edges of the image, which are limited to the spine. These aforementioned works [14; 15] show that a computer-aided method provides more accurate results for fracture detection and has the potential to accelerate the assessment of trauma cases, reduce the possibility of misclassification of bone fractures, and reduce variability between observers. But, these methods cannot be applied to the calcaneus bones, which have different shapes, features, and types of fracture than their study. In the case of the calcaneus, segmentation and detection of the calcaneal fracture are very challenging, due to the low resolution of CT images, the complex anatomy, and soft tissue structures of the calcaneus [16; 17]. At present, there are deficiencies in the operative or conservative management planning of the calcaneus, which are caused by the lack of a standard system in the classification of calcaneal fractures [18]. A study to apply image processing for the detection and classification of calcaneal fractures has not been widely performed. [PERSON] et al. [19] has proposed automated classification and detection of calcaneus bone fracture locations in CT images.