**Featured Application: Segmentation and classification of calcaneal fractures.**

**Abstract:** Calcaneal fractures often occur because of accidents during exercise or activities. In general, the detection of the calcaneal 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 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.

**Keywords:** biomedical imaging; bone fracture; calcaneus; CT image; segmentation

#### **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 fascial, 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. Wu 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. Roth 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. Pranata et al. [19] has proposed automated classification and detection of calcaneus bone fracture locations in CT images. 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 [10–12]. 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 fractures in 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.

#### **2. Materials and Methods**

#### *2.1. Materials*

We obtained 2210 CT Digital Imaging and Communications in Medicine (DICOM) images from the datasets with assessment and evaluation of the fracture type information [1–13], radiopedia (https:// radiopaedia.org/), anesthesia key (https://aneskey.com/fractures-and-dislocations-of-the-tarsal-bones/), and CT images from two patients with three anatomical plane orientations. The dataset consists of 815 coronal images, 777 transverse images, and 618 sagittal images. The experiment was conducted using Visual Studio C++ 2017 in the 3.40 GHz CPU with 8 GB RAM.
