Real-Time Automated Segmentation and Classification of Calcaneal Fractures in CT Images

Abstract

Calcaneus fractures often occur because of accidents during exercise or activities. In general, the detection of the calcaneus 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 the 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.

Real-Time Detection and Recognition of Multiple Moving Objects for Aerial Surveillance

“…Step 2: People and cars are detected in the images as the moving objects candidates or foreground. In this step, Haar-like features [37] and cascade classifiers [38,39] are used to detect and recognize the objects in the images and determine the region of interest (ROI) for the objects. This is followed by labeling the background and foreground.…”

Special Issue on Intelligent Processing on Image and Optical Information

“…In general, the detection of calcaneal fractures is still performed manually through computer tomography (CT) image observation. Wahyu Rahmaniar and Wen-June Wang [5], in their paper, 'Real-Time Automated Segmentation and Classification of Calcaneal Fractures in CT Images' proposed a method for detecting calcaneal fractures through localization and color segmentation of calcaneus.…”

Ground Truth Annotated Femoral X-Ray Image Dataset and Object Detection Based Method for Fracture Types Classification

“…Fracture classification systems are now mostly dependent on three-dimensional computed tomography (CT), X-ray and magnetic resonance images (MRI) and manual visual inspection according to AO classification [3]. However, the information of these images maybe accurate enough for surgeon to detect some clear fraction but not to examine small fractures because of their low resolutions [4]. Therefore, there are several computer-aided algorithms involved to assist in classifying the fractures and supply more quantitative information.…”

“…Lucas et al leverage Bayes classifier with texture, shape and statistical features to make a diagnosis of benign and malignant vertebral compression fractures (VCFs) in MRIs [7]. Wahyu et al present a real-time automated segmentation and classification methods of calcaneal fractures in CT images based on the amount of the fragments and the location of the lines fractures [4].…”

“…The model is fed with many medical images with correct labels during training [12][13][14]. After that, it can quickly identify the special areas and pathological changes in the image, which provides doctors with a second choice [4][5][6]. The most popular area of research in AI are pattern detection and image interpretation, which are widely used in various conditions of orthopedics， including diagnosis of bone fracture [15], automatic detection of knee joints and quantification of knee osteoarthritis severity, assessment of bone strength and quality through evaluating trabecular bone microarchitecture [16][17].…”