Variability of Cobb angle measurement from digital X-ray image based on different de-noising techniques

Mukherjee, Jhilam; Kundu, Raka; Chakrabarti, Amlan

doi:10.1504/ijbet.2014.065656

Cited by 16 publications

(9 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We proposed an automated method for estimation of Cobb angles from X-ray scans. Contrary to most other approaches, our approach measured Cobb angle directly from the centerline of the spine without requiring the segmentation of individual vertebrae [3,4,8].…”

Section: Discussionmentioning

confidence: 99%

“…The centerline was then smoothed and its derivative was computed to obtain tangents and estimate the Cobb angles. Unlike our approach, most automated methods for estimating Cobb angles rely on the segmentation of the individual vertebrae or the detection of their corners [3,4,8]. Recently, Wu et al [3] predicted the position of landmarks indicating the corners of vertebrae using convolution neural networks (CNNs).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Automated Estimation of the Spinal Curvature via Spine Centerline Extraction with Ensembles of Cascaded Neural Networks

Dubost

Collery

Renaudier

et al. 2020

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Scoliosis is a condition defined by an abnormal spinal curvature. For diagnosis and treatment planning of scoliosis, spinal curvature can be estimated using Cobb angles. We propose an automated method for the estimation of Cobb angles from X-ray scans. First, the centerline of the spine was segmented using a cascade of two convolutional neural networks. After smoothing the centerline, Cobb angles were automatically estimated using the derivative of the centerline. We evaluated the results using the mean absolute error and the average symmetric mean absolute percentage error between the manual assessment by experts and the automated predictions. For optimization, we used 609 X-ray scans from the London Health Sciences Center, and for evaluation, we participated in the international challenge "Accurate Automated Spinal Curvature Estimation, MICCAI 2019" (100 scans). On the challenge's test set, we obtained an average symmetric mean absolute percentage error of 22.96.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Automated Estimation of the Spinal Curvature via Spine Centerline Extraction with Ensembles of Cascaded Neural Networks

Dubost

Collery

Renaudier

et al. 2020

Lecture Notes in Computer Science

View full text Add to dashboard Cite

show abstract

“…Mukherjee et al [9] selected the best filter of the four denoising techniques: bilateral filters [10], nonlocal means filters [11], principal neighborhood dictionaries nonlocal means filtering [12], and block matching three-dimensional filtering [13]. Due to the poor contrast of radiographs, histogram equalization was applied to enhance image contrast, and the Otsu thresholding method was used to find the Canny edge points of vertebrae.…”

Section: Introductionmentioning

confidence: 99%

Cobb Angle Measurement of Spine from X-Ray Images Using Convolutional Neural Network

Horng

Kuok

et al. 2019

Computational and Mathematical Methods in Medicine

198

121

View full text Add to dashboard Cite

Scoliosis is a common spinal condition where the spine curves to the side and thus deforms the spine. Curvature estimation provides a powerful index to evaluate the deformation severity of scoliosis. In current clinical diagnosis, the standard curvature estimation method for assessing the curvature quantitatively is done by measuring the Cobb angle, which is the angle between two lines, drawn perpendicular to the upper endplate of the uppermost vertebra involved and the lower endplate of the lowest vertebra involved. However, manual measurement of spine curvature requires considerable time and effort, along with associated problems such as interobserver and intraobserver variations. In this article, we propose an automatic system for measuring spine curvature using the anterior-posterior (AP) view spinal X-ray images. Due to the characteristic of AP view images, we first reduced the image size and then used horizontal and vertical intensity projection histograms to define the region of interest of the spine which is then cropped for sequential processing. Next, the boundaries of the spine, the central spinal curve line, and the spine foreground are detected by using intensity and gradient information of the region of interest, and a progressive thresholding approach is then employed to detect the locations of the vertebrae. In order to reduce the influences of inconsistent intensity distribution of vertebrae in the spine AP image, we applied the deep learning convolutional neural network (CNN) approaches which include the U-Net, the Dense U-Net, and Residual U-Net, to segment the vertebrae. Finally, the segmentation results of the vertebrae are reconstructed into a complete segmented spine image, and the spine curvature is calculated based on the Cobb angle criterion. In the experiments, we showed the results for spine segmentation and spine curvature; the results were then compared to manual measurements by specialists. The segmentation results of the Residual U-Net were superior to the other two convolutional neural networks. The one-way ANOVA test also demonstrated that the three measurements including the manual records of two different physicians and our proposed measured record were not significantly different in terms of spine curvature measurement. Looking forward, the proposed system can be applied in clinical diagnosis to assist doctors for a better understanding of scoliosis severity and for clinical treatments.

show abstract

“…After LCM segmentation, the spine was cropped, and the color subtraction technique was used to create a binary mask. The spinal curvature was traced by finding the coordinates of the binary mask edges using the Canny edge detection algorithm [ 23 ]. The method of separating the spine region and finding the center curve of the spine is described in Fig 8 .…”

Section: Resultsmentioning

confidence: 99%

“…Automatic Cobb angle measurement is a computerized method utilized to determine the degree of spinal curvature, specifically for evaluating the severity of scoliosis [19]. Existing methods for Cobb angle measurement can be time-consuming and labor-intensive [20][21][22][23][24][25]. Earlier researchers have proposed automatic Cobb angle measurement methods that rely on extensive datasets, adding to the labor-intensive nature of the process [26,27].…”

Section: Introductionmentioning

confidence: 99%

Unsupervised local center of mass based scoliosis spinal segmentation and Cobb angle measurement

Sikkandar,

Alhashim,

Alassaf

et al. 2024

PLoS ONE

View full text Add to dashboard Cite

Scoliosis is a medical condition in which a person’s spine has an abnormal curvature and Cobb angle is a measurement used to evaluate the severity of a spinal curvature. Presently, automatic Existing Cobb angle measurement techniques require huge dataset, time-consuming, and needs significant effort. So, it is important to develop an unsupervised method for the measurement of Cobb angle with good accuracy. In this work, an unsupervised local center of mass (LCM) technique is proposed to segment the spine region and further novel Cobb angle measurement method is proposed for accurate measurement. Validation of the proposed method was carried out on 2D X-ray images from the Saudi Arabian population. Segmentation results were compared with GMM-Based Hidden Markov Random Field (GMM-HMRF) segmentation method based on sensitivity, specificity, and dice score. Based on the findings, it can be observed that our proposed segmentation method provides an overall accuracy of 97.3% whereas GMM-HMRF has an accuracy of 89.19%. Also, the proposed method has a higher dice score of 0.54 compared to GMM-HMRF. To further evaluate the effectiveness of the approach in the Cobb angle measurement, the results were compared with Senior Scoliosis Surgeon at Multispecialty Hospital in Saudi Arabia. The findings indicated that the segmentation of the scoliotic spine was nearly flawless, and the Cobb angle measurements obtained through manual examination by the expert and the algorithm were nearly identical, with a discrepancy of only ± 3 degrees. Our proposed method can pave the way for accurate spinal segmentation and Cobb angle measurement among scoliosis patients by reducing observers’ variability.

show abstract

Variability of Cobb angle measurement from digital X-ray image based on different de-noising techniques

Cited by 16 publications

References 18 publications

Automated Estimation of the Spinal Curvature via Spine Centerline Extraction with Ensembles of Cascaded Neural Networks

Automated Estimation of the Spinal Curvature via Spine Centerline Extraction with Ensembles of Cascaded Neural Networks

Cobb Angle Measurement of Spine from X-Ray Images Using Convolutional Neural Network

Unsupervised local center of mass based scoliosis spinal segmentation and Cobb angle measurement

Contact Info

Product

Resources

About