Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Automatic segmentation of wrist bone fracture area by K-means pixel clustering from X-ray image
11
Zitationen
3
Autoren
2019
Jahr
Abstract
Early detection of subtle fracture is important particularly for the senior citizens’ quality of life. Naked eye examination from X-ray image may cause false negatives due to operator subjectivity thus computer vision based automatic detection software is much needed in practice. In this paper, we propose an automatic extraction method for suspisious wrist fracture regions. We apply K-means in pixel clustering to form the candidate part of possible fracture from wrist X-ray image automatically. This method can recover previously detected patterned false cases with edge detection method after fuzzy stretching. The proposed method is successful in 16 out of 20 tested cases in experiment.
Ähnliche Arbeiten
A survey on deep learning in medical image analysis
2017 · 14.008 Zit.
nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation
2020 · 8.160 Zit.
Calculation of average PSNR differences between RD-curves
2001 · 4.093 Zit.
Magnetic Resonance Classification of Lumbar Intervertebral Disc Degeneration
2001 · 3.942 Zit.
Vertebral fracture assessment using a semiquantitative technique
1993 · 3.633 Zit.