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http://yadda.icm.edu.pl:80/baztech/element/bwmeta1.element.baztech-bf9cd542-017d-4da3-8d54-8867db0c48d4

Czasopismo

Biocybernetics and Biomedical Engineering

Tytuł artykułu

DIFFRACT: DIaphyseal Femur FRActure Classifier SysTem

Autorzy Bayram, F.  Çakıroğlu, M. 
Treść / Zawartość http://www.ibib.waw.pl/pl/wydawnictwa/biocybernetics-and-biomedical-enginering-bbe/bbe-tomy http://www.journals.elsevier.com/biocybernetics-and-biomedical-engineering/
Warianty tytułu
Języki publikacji EN
Abstrakty
EN Determining the types of fractured bones is the most important step of fracture treatment. Different fracture cases may be observed in daily life and each of them may require a specific treatment. It is not possible for a physician to know all fracture types and treatment methods by heart. Therefore, it is needed an effective solution to facilitate such a tedious process. Based on this need, we propose an auxiliary tool called a DIaphyseal Femur FRActure Classifier SysTem (DIFFRACT). The DIFFRACT can automatically classify diaphyseal femur fractures according to the Müller AO Classification system on X-ray images. In DIFFRACT, we have used the Niblack thresholding method to segment X-ray images. We have observed that Niblack is the most effective method for the segmentation of fractured bones since it does not lose information related to the fracture region. Moreover, we have developed a novel pre-processing method called a support vector machine (SVM) based sensitive noise remover to remove the noises occurring in the segmentation step. In addition, we have innovatively proposed two combined feature extraction methods, the bone completeness indicator (BCI) and fractured region mapping (FRM), to classify different types of fractures. We have used a multi-class SVM to determine the type of bone fractures. Based on the detailed experiments, 196 X-ray images were classified into nine classes according to AO-32 with 89.87% success rate. The DIFFRACT may be used as supplementary tool for the determination of fractured femur bones by physicians. It may facilitate decision making process of the physicians.
Słowa kluczowe
PL komputerowe wspomaganie diagnozy   przetwarzanie obrazu   maszyna wektorów wspierających  
EN femoral diaphyseal fracture   computer aided diagnosis   classification   image processing   support vector machine (SVM)  
Wydawca Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences
Elsevier
Czasopismo Biocybernetics and Biomedical Engineering
Rocznik 2016
Tom Vol. 36, no. 1
Strony 157--171
Opis fizyczny Bibliogr. 35 poz., rys., tab., wykr.
Twórcy
autor Bayram, F.
  • Department of Electronic and Computer Education, Institute of Natural Sciences, Sakarya University, Esentepe Kampüsü, 54187 Sakarya, Turkey, bayramfatih@gmail.com
autor Çakıroğlu, M.
  • Department of Mechatronic Engineering, Faculty of Technology, Sakarya University, Esentepe Kampüsü, Sakarya, Turkey, muratc@sakarya.edu.tr
Bibliografia
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Uwagi
PL Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę.
Kolekcja BazTech
Identyfikator YADDA bwmeta1.element.baztech-bf9cd542-017d-4da3-8d54-8867db0c48d4
Identyfikatory
DOI 10.1016/j.bbe.2015.10.003