Shape, texture and statistical features for classification of benign and malignant vertebral compression fractures in magnetic resonance images.
Vertebral compression fractures (VCFs) result in partial collapse of vertebral bodies. They usually are nontraumatic or occur with low-energy trauma in the elderly secondary to different etiologies, such as insufficiency fractures of bone fragility in osteoporosis (benign fractures) or vertebral metastasis (malignant fractures). Our study aims to classify VCFs in T1-weighted magnetic resonance images (MRI).



We used the median sagittal planes of lumbar spine MRIs from 63 patients (38 women and 25 men) previously diagnosed with VCFs. The lumbar vertebral bodies were manually segmented and statistical features of gray levels were computed from the histogram. We also extracted texture and shape features to analyze the contours of the vertebral bodies. In total, 102 lumbar VCFs (53 benign and 49 malignant) and 89 normal lumbar vertebral bodies were analyzed. The k-nearest-neighbor method, a neural network with radial basis functions, and a naïve Bayes classifier were used with feature selection. We compared the classification obtained by these classifiers with the final diagnosis of each case, including biopsy for the malignant fractures and clinical and laboratory follow up for the benign fractures.



The results obtained show an area under the receiver operating characteristic curve of 0.97 in distinguishing between normal and fractured vertebral bodies, and 0.92 in discriminating between benign and malignant fractures.



The proposed classification methods based on shape, texture, and statistical features have provided high accuracy and may assist in the diagnosis of VCFs.
Image Science and Medical Physics Center, Internal Medicine Department, Ribeirão Preto Medical School, University of São Paulo, 3900 Bandeirantes Avenue, Ribeirão Preto, SP 14048-900, Brazil.
Lucas
L
Frighetto-Pereira
Department of Electrical and Computer Engineering, Schulich School of Engineering, University of Calgary, 2500 University Dr. NW, Calgary, AB, Canada T2N 1N4.
Rangaraj Mandayam
RM
Rangayyan
Image Science and Medical Physics Center, Internal Medicine Department, Ribeirão Preto Medical School, University of São Paulo, 3900 Bandeirantes Avenue, Ribeirão Preto, SP 14048-900, Brazil.
Guilherme Augusto
GA
Metzner
Image Science and Medical Physics Center, Internal Medicine Department, Ribeirão Preto Medical School, University of São Paulo, 3900 Bandeirantes Avenue, Ribeirão Preto, SP 14048-900, Brazil.
Paulo Mazzoncini
PM
de Azevedo-Marques
Image Science and Medical Physics Center, Internal Medicine Department, Ribeirão Preto Medical School, University of São Paulo, 3900 Bandeirantes Avenue, Ribeirão Preto, SP 14048-900, Brazil. Electronic address: marcello@fmrp.usp.br.
Marcello Henrique
MH
Nogueira-Barbosa
D005260:Female; D050815:Fractures, Compression / Q000145:classification / Q000000981:diagnostic imaging; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008159:Lumbar Vertebrae / Q000000981:diagnostic imaging*; D008279:Magnetic Resonance Imaging; D008297:Male; D016103:Spinal Fractures / Q000145:classification / Q000000981:diagnostic imaging
Computer-aided diagnosis; Image processing; Magnetic resonance images; Shape analysis; Statistical analysis of gray levels; Texture analysis; Vertebral compression fractures