Semiautomatic Classification of Benign Versus Malignant Vertebral Compression Fractures Using Texture and Gray-Level Features in Magnetic Resonance Images

Abstract

Our study aimed to develop a system for computer-aided diagnosis of vertebral compression fractures (VCFs) using magnetic resonance imaging (MRI), to help in the differentiation between malignant and benign VCFs. Lumbar spine MRI was used to acquire T1-weighted images in the sagittal plane. Images from 63 consecutive patients (38 women, 25 men, mean age 62.25 ± 14.13 years) with at least one VCF diagnosis were studied. Contrast and texture features were extracted from manually segmented images of 103 vertebral bodies with VCFs. The classification of malignant vs. benign VCFs was performed using the k-nearest neighbor (KNN) classifier with the Euclidean distance. Using a KNN classifier with k=3, feature selection, and 10-fold cross-validation, we obtained a value of the area under the receiver operating characteristic curve of 0.913.

Portable and Low-cost Hologram Verification Module That Uses Hyperspectral Imaging

“…In a color image, the gray value can have values between 0 and 255. In the past, many studies used MGVs for image classi cation and detection, because it is one of the reliable classi cation methods [40,[42][43][44]. Figure 6 shows the mean of the duplicate and original samples between 400 nm and 700 nm (see Supplement 1 Section 3 for the detailed plot of all the samples).…”

Two-Stream Compare and Contrast Network for Vertebral Compression Fracture Diagnosis

“…differentiating between benign and malignant VCFs. Most of the previous studies only solve one of the tasks [7], [9]- [12]. Frighetto et al [13] solve both two tasks but in a two-step manner, i.e.…”

Analysis of medical images to support decision-making in the musculoskeletal field

“…A very recurrent disease among older adults is Vertebral Compression Fractures (VCF), which, in general, is caused by bone-weakening diseases. The literature reports the works (BARBIERI et al, 2015;CASTI et al, 2017;FRIGHETTO-PEREIRA et al, 2015;FRIGHETTO-PEREIRA et al, 2015;AZEVEDO-MARQUES et al, 2015;FRIGHETTO-PEREIRA et al, 2016a) that employ computational methods for VCF analysis from MRIs exams. The works used, unless stated otherwise, a database with 63 T1 weighted exams (detailed in Section 3.2.1).…”

“…Classification: the works (FRIGHETTO-PEREIRA et al, 2015;FRIGHETTO-PEREIRA et al, 2015;AZEVEDO-MARQUES et al, 2015;FRIGHETTO-PEREIRA et al, 2016a) dealt with the classification of VCFs using manually segmented vertebral bodies in 2D (slices) exams. Casti et al (2017) proposed a cooperative strategy that exploited the dynamic contribution of the classification models, presenting an AUC of 94% using QDA classifier and considering the reference manual segmentation (ground-truth).…”