Shape, texture and statistical features for classification of benign and malignant vertebral compression fractures in magnetic resonance images

Autor(es)

Frighetto-Pereira, Lucas ^[1] ; Rangayyan, Rangaraj Mandayam ^[2] ; Metzner, Guilherme Augusto ^[1] ; de Azevedo-Marques, Paulo Mazzoncini ^[1] ; Nogueira-Barbosa, Marcello Henrique ^[1]

Afiliação do(s) autor(es)

^[1] Univ Sao Paulo, Ribeirao Preto Med Sch, Dept Internal Med, Image Sci & Med Phys Ctr, 3900 Bandeirantes Ave, BR-14048900 Ribeirao Preto, SP - Brazil

^[2] Univ Calgary, Schulich Sch Engn, Dept Elect & Comp Engn, 2500 Univ Dr NW, Calgary, AB T2N 1N4 - Canada

• Tipo de documento: Artigo Científico
• Fonte: COMPUTERS IN BIOLOGY AND MEDICINE; v. 73, p. 147-156, JUN 1 2016.
• Citações Web of Science: 8

Resumo

Purpose: 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). Methods: 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 naive 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. Results: 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. Conclusions: The proposed classification methods based on shape, texture, and statistical features have provided high accuracy and may assist in the diagnosis of VCFs. (C) 2016 Elsevier Ltd. All rights reserved. (AU)

• Processo FAPESP: 15/08778-6 - Classificação semiautomática de fraturas vertebrais por compressão utilizando técnicas de forma de processamento de imagens em imagens de ressonância magnética.
• Beneficiário: Lucas Frighetto Pereira
• Modalidade de apoio: Bolsas no Exterior - Estágio de Pesquisa - Mestrado
• Processo FAPESP: 14/12135-0 - Classificação semiautomática de fraturas vertebrais benignas e malignas em imagens de ressonância magnética
• Beneficiário: Lucas Frighetto Pereira
• Modalidade de apoio: Bolsas no Brasil - Mestrado