# Real-Time Automated Segmentation and Classification of Calcaneal Fractures in CT Images
## 3 Experimental Results and Discussion  
Figures 17 and 18a show the calcaneus classified as non-fracture and Type I, in which both have one segmented region, represented by only one color of the calcaneus segmentation. In non-fractured calcaneus images, the calcaneus region may be fully segmented. In Type I fracture images, the calcaneus region cannot be fully segmented, as there are several fracture lines in the calcaneus body. Figure 18 shows each color on the calcaneus segmentation representing one fragment. Although the type of fracture can be determined based on the number of fragments, the location of the fragments in the calcaneus is different with different shapes. In images of Type II fractures, failure of segmentation results will cause errors in the detection of fracture types. Thus, the calcaneus will be detected as a Type III fracture. This is due to the ambiguity in the bone structure that causes errors in separating a single blob into two blobs. In images of Type III fractures, failure of segmentation  
Figure 16: (**a**) Two fragments are in the same region; (**b**) The largest fragment is selected; and (**c**) The fracture type is based on the area between these two fragments. Figure 17: Normal calcaneus segmentation: (**a**) Coronal; (**b**) Transverse; and (**c**) Sagittal. Figure 14: Steps to determine the type of fracture in case of a line fracture: (**a**) The region of the fracture line is represented by a yellow box and (**b**) The fracture type is based on the line region. Figure 15: Steps to determine the type of fracture in case of fragment fracture: (**a**) The region of the fragment is represented by a yellow box and (**b**) The fracture type is based on the area between the two fragments. results will cause errors in detecting two blobs as a single blob. This is due to the ambiguity of the CT image, which makes the algorithm unable to separate the white pixels between the two fragments. Due to several factors mentioned, the most accurate detection results relate to Type IV fracture images. Since Type IV fractures have more than three fragments, some errors in the segmentation results do not significantly affect the detection of fracture types, which are determined by the number of fragments. Table 1 shows the accuracy of fracture detection in coronal and transverse images. In coronal images, fragment separation is shown more clearly than in transverse images. In transverse images, the bone structure is slightly ambiguous, thus making segmentation fail to separate the foreground (bone structure) and background. If the segmentation fails, the fragment region cannot be filled as a single blob and it will be considered as an additional fragment. The proposed method achieves an average accuracy of 87.25% for coronal images and 83.25% for transverse images. Figure 19 shows the fracture segmentation results in sagittal images. Table 2 shows the accuracy of the fracture type detection in sagittal images. In sagittal images, fractures in the calcaneus region \begin{table}
\begin{tabular}{c c c} \hline
**Fractures Type** & **Coronal (\%)** & **Transverse (\%)** \\ \hline Type I & 88 & 84 \\ Type II & 86 & 81 \\ Type III & 83 & 80 \\ Type IV & 92 & 88 \\ \hline Average & 87.25 & 83.25 \\ \hline \end{tabular}
\end{table} 
Table 1: Accuracy of the fractures type detection based on [PERSON] classification. Figure 18: Segmentation result of the calcaneal fractures based on [PERSON] classification: (**a**) Type I; (**b**) Type II; (**c**) Type III; and (**d**) Type IV. are too ambiguous and almost similar to the bone structure. Common mistakes in fracture detection in sagittal image are as follows:  
* Bone structure is detected as a fracture,
* The fracture in the image is too similar to the bone structure, so it is not recognized as a fracture. The most accurate results in sagittal image is for Type B fractures, where the area of the fracture is located in the middle of the calcaneus.