TY - JOUR
T1 - Automatic classifying of patients with non-traumatic fractures based on ultrasonic guided wave spectrum image using a dynamic support vector machine
AU - Minonzio, Jean Gabriel
AU - Cataldo, Bryan
AU - Olivares, Rodrigo
AU - Ramiandrisoa, Donatien
AU - Soto, Ricardo
AU - Crawford, Broderick
AU - de Albuquerque, Victor Hugo C.
AU - Munoz, Roberto
N1 - Publisher Copyright:
© 2020 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.
PY - 2020
Y1 - 2020
N2 - Bone fractures are caused by diseases or accidents and are a widespread problem throughout the globe. Worldwide, 1.6 millions of hip fractures occur every year and are expected to rise to 6.3 millions in 2050. The current gold standard to assess fracture risk is the Dual-energy X-ray Absorptiometry (DXA), which provides a projected image of the bone from which areal bone mineral density is extracted. Ultrasound techniques have been proposed as non invasive alternatives. Recently, estimates of cortical thickness and porosity, obtained by Bi-Directional Axial Transmission (BDAT) in a pilot clinical study, have been shown to be associated with non-traumatic fractures in post menopausal women. Cortical parameters were derived from the comparison between experimental and theoretical guided modes. This model-based inverse approach failed for the patients associated with poor guided mode information. Moreover, even if the fracture discrimination ability was found similar to DXA, it remained moderate. The goal of this paper is to explore if these two limitations could be overcome by using automatic classification tools, independent of any waveguide model. To this end, a dynamic machine learning approach based on a Support Vector Machine (SVM) has been used to classify ultrasonic guided wave spectrum images measured by BDAT on post menopausal women with or without non-traumatic fractures. This approach has then been improved using parameters tuned by Bat Algorithm Optimization (BOA). The applied methodology focused on the extraction of texture features through a gray level co-occurrence matrix, structural comparison and histograms. The results accuracy was assessed using a confusion matrix. The effectiveness of the learning approach reached an accuracy of 92.31%.
AB - Bone fractures are caused by diseases or accidents and are a widespread problem throughout the globe. Worldwide, 1.6 millions of hip fractures occur every year and are expected to rise to 6.3 millions in 2050. The current gold standard to assess fracture risk is the Dual-energy X-ray Absorptiometry (DXA), which provides a projected image of the bone from which areal bone mineral density is extracted. Ultrasound techniques have been proposed as non invasive alternatives. Recently, estimates of cortical thickness and porosity, obtained by Bi-Directional Axial Transmission (BDAT) in a pilot clinical study, have been shown to be associated with non-traumatic fractures in post menopausal women. Cortical parameters were derived from the comparison between experimental and theoretical guided modes. This model-based inverse approach failed for the patients associated with poor guided mode information. Moreover, even if the fracture discrimination ability was found similar to DXA, it remained moderate. The goal of this paper is to explore if these two limitations could be overcome by using automatic classification tools, independent of any waveguide model. To this end, a dynamic machine learning approach based on a Support Vector Machine (SVM) has been used to classify ultrasonic guided wave spectrum images measured by BDAT on post menopausal women with or without non-traumatic fractures. This approach has then been improved using parameters tuned by Bat Algorithm Optimization (BOA). The applied methodology focused on the extraction of texture features through a gray level co-occurrence matrix, structural comparison and histograms. The results accuracy was assessed using a confusion matrix. The effectiveness of the learning approach reached an accuracy of 92.31%.
KW - Bat algorithm
KW - Cortical bone
KW - Fracture discrimination
KW - Guided wave spectrum image
KW - Guided waves
KW - Osteoporosis
KW - Quantitative ultrasound
KW - Support vector machine
UR - http://www.scopus.com/inward/record.url?scp=85102752869&partnerID=8YFLogxK
U2 - 10.1109/ACCESS.2020.3033480
DO - 10.1109/ACCESS.2020.3033480
M3 - Article
AN - SCOPUS:85102752869
SN - 2169-3536
VL - 8
SP - 194752
EP - 194764
JO - IEEE Access
JF - IEEE Access
ER -