NDLI: Classification of vertebral compression fractures in magnetic resonance images using spectral and fractal analysis

Content Provider	IEEE Xplore Digital Library
Author	Azevedo-Marques, P.M. Spagnoli, H.F. Frighetto-Pereira, L. Menezes-Reis, R. Metzner, G.A. Rangayyan, R.M. Nogueira-Barbosa, M.H.
Copyright Year	2015
Description	Author affiliation: Dept. of Electr. & Comput. Eng., Univ. of Calgary, Calgary, AB, Canada (Rangayyan, R.M.) \|\| Ribeirao Preto Med. Sch., Univ. of Sao Paulo, Ribeirão Preto, Brazil (Azevedo-Marques, P.M.; Spagnoli, H.F.; Frighetto-Pereira, L.; Menezes-Reis, R.; Metzner, G.A.; Nogueira-Barbosa, M.H.)
Abstract	Fractures with partial collapse of vertebral bodies are generically referred to as “vertebral compression fractures” or VCFs. VCFs can have different etiologies comprising trauma, bone failure related to osteoporosis, or metastatic cancer affecting bone. VCFs related to osteoporosis (benign fractures) and to cancer (malignant fractures) are commonly found in the elderly population. In the clinical setting, the differentiation between benign and malignant fractures is complex and difficult. This paper presents a study aimed at developing a system for computer-aided diagnosis to help in the differentiation between malignant and benign VCFs in magnetic resonance imaging (MRI). We used T1-weighted MRI of the lumbar spine in the sagittal plane. Images from 47 consecutive patients (31 women, 16 men, mean age 63 years) were studied, including 19 malignant fractures and 54 benign fractures. Spectral and fractal features were extracted from manually segmented images of 73 vertebral bodies with VCFs. The classification of malignant vs. benign VCFs was performed using the k-nearest neighbor classifier with the Euclidean distance. Results obtained show that combinations of features derived from Fourier and wavelet transforms, together with the fractal dimension, were able to obtain correct classification rate up to 94.7% with area under the receiver operating characteristic curve up to 0.95.
Starting Page	723
Ending Page	726
File Size	744359
Page Count	4
File Format	PDF
ISSN	1557170X
e-ISBN	9781424492718
DOI	10.1109/EMBC.2015.7318464
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-08-25
Publisher Place	Italy
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Cancer Magnetic resonance imaging Feature extraction Fractals Image coding Image segmentation Biomedical imaging
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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