Automated Fractured Bone Segmentation and Labeling from CT Images
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Within the scope of education and training, automatic and accurate segmentation of fractured bones from Computed Tomographic (CT) images is the fundamental step in several different applications, such as trauma analysis, visualization, diagnosis, surgical planning and simulation. It helps physicians analyze the severity of injury by taking into account the following fracture features, such as location of the fracture, number of pieces and deviation from the original location. Besides, it helps provide accurate 3D visualization and decide optimal recovery plans/processes. To accurately segment fracture bones from CT images, in the paper, we introduce a segmentation technique that makes labeling process easier. Based on the patient-specific anatomy, unique labels are assigned. Unlike conventional techniques, it also includes the removal of unwanted artifacts, such as flesh. In our experiments, we have demonstrated our concept with real-world data (with an accuracy of 95.45%) and have compared with state-of-the-art techniques. For validation, our tests followed expert-based decisions i.e., clinical ground-truth. With the results, our collection of 8000 CT images will be available upon the request.
KeywordsCT images Fractured bones Contrast stretching Histogram modeling Connected component Segmentation Hierarchical structured labeling
Authors thank the Ministry of Electronics and Information Technology (MeitY), New Delhi for granting Visvesvaraya Ph.D. fellowship through file no. PhD-MLA ∖ 4(34) ∖ 201-1 Dated: 05/11/2015. The first author would like to thank Dr. Jamma and Dr. Jagtap for providing expert guidance on bone anatomy. Along with this, he also would like to thank, Prism Medical Diagnostics lab, Chhatrapati Shivaji Maharaj Sarvopachar Ruganalay and Ashwini Hospital for providing patient-specific CT images.
The study was partially funded by the Ministry of Electronics and Information Technology (MeitY), New Delhi for granting Visvesvaraya Ph.D. fellowship through file no. PhD-MLA ∖ 4(34) ∖ 201-1 Dated: 05/11/2015.
Compliance with Ethical Standards
Conflict of interests
Authors declared no conflict of interest.
This article does not contain any studies with human participants performed by any of the authors.
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