Advances in MR imaging of vertebral collapse

Tehranzadeh, Jamshid; Tao, Cliff

doi:10.1053/j.sult.2004.09.002

Cited by 27 publications

(9 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The elderly population has higher incidence and prevalence of VCFs secondary to bone failure than the younger population. Such bone failure may be related to osteoporosis or bone metastasis [1]. Osteoporotic VCFs are an important risk factor for additional future osteoporotic fractures and are also linked to increased mortality [2].…”

Section: Introductionmentioning

confidence: 99%

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

Frighetto-Pereira

Menezes-Reis

Metzner

et al. 2015

2015 IEEE 28th International Symposium on Computer-Based Medical Systems

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%

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

Frighetto-Pereira

Menezes-Reis

Metzner

et al. 2015

2015 IEEE 28th International Symposium on Computer-Based Medical Systems

View full text Add to dashboard Cite

show abstract

“…Non-traumatic Vertebral Compression Fractures (VCFs) generally result from osteoporosis (benign VCFs) and metastatic cancer (malignant VCFs). The diagnosis of non-traumatic VCFs is made by radiologists generally through the use of magnetic resonance images (MRI), allowing the professional to differentiate malignant and benign VCFs [Tehranzadeh and Tao 2004].…”

Section: Introductionmentioning

confidence: 99%

Impact of Feature Selection on Clustering Images ofVertebral Compression Fractures

Candido¹,

Lama²,

Chiari³

et al. 2020

Anais Do Encontro Nacional De Inteligência Artificial E Computacional (ENIAC 2020)

View full text Add to dashboard Cite

Non-traumatic Vertebral Compression Fractures (VCFs) are generally caused by osteoporosis (benign VCFs) or metastatic cancer (malignant VCFs) and the success of the medical treatment strongly depends on a fast and correct classification of VCFs. Recently, methods for computer-aided diagnosis (CAD) based on machine learning have been proposed for classifying VCFs. In this work, we investigate the problem of clustering images of VCFs and the impact of feature selection by genetic algorithms, comparing the clustering i)with all features and ii)with feature selection through the purity results. The analysis of the clusters helps to understand the results of classifiers and difficulties of differentiating images of different classes by an expert. The results indicate that features selection improved the separability of clusters and purity. Feature selection also helps to understand which attributes are most important for analysing the images of vertebral bodies.

show abstract

“…Spinal diseases are quite usual worldwide and can cause significant loss of function and quality of life (1). A very recurrent disease among older adults is the Vertebral Compression Fracture (VCF), which, in general, is caused by osteoporosis (benign) or bone metastasis (malignant) (2,3,4). In general, VCFs are early detected or diagnosed based on shape or texture using Magnetic Resonance Imaging (MRI) (5,6,3,7).…”

Section: Introductionmentioning

confidence: 99%

BGrowth

Ramos

Watanabe

Nogueira-Barbosa

et al. 2019

Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing

View full text Add to dashboard Cite

Segmentation of medical images is a critical issue: several process of analysis and classification rely on this segmentation. With the growing number of people presenting back pain and problems related to it, the automatic or semi-automatic segmentation of fractured vertebral bodies became a challenging task. In general, those fractures present several regions with non-homogeneous intensities and the dark regions are quite similar to the structures nearby. Aimed at overriding this challenge, in this paper we present a semi-automatic segmentation method, called Balanced Growth (BGrowth). The experimental results on a dataset with 102 crushed and 89 normal vertebrae show that our approach significantly outperforms well-known methods from the literature. We have achieved an accuracy up to 95% while keeping acceptable processing time performance, that is equivalent to the state-of-the-art methods. Moreover, BGrowth presents the best results even with a rough (sloppy) manual annotation (seed points).

show abstract

Advances in MR imaging of vertebral collapse

Cited by 27 publications

References 34 publications

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

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

Impact of Feature Selection on Clustering Images ofVertebral Compression Fractures

BGrowth

Contact Info

Product

Resources

About