Disc bulge diagnostic model in axial lumbar MR images using Intervertebral disc Descriptor (IdD)

Beulah, A.; Sharmila, T. Sree; Pramod, V. K.

doi:10.1007/s11042-018-5914-8

Cited by 14 publications

(13 citation statements)

References 17 publications

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“…Some of the cases in the study included CT imaging data. In the study, we found that, according to the measurement method developed by Beulah et al [ 19 ], the joint angle on the horizontal axis of MRI meets the requirements of sports. The clarity of the facet joint image can fully meet the measurement requirements.…”

Section: Mri Study Of Lumbar Disc Herniationmentioning

confidence: 99%

The Clinical Diagnostic Value of Lumbar Intervertebral Disc Herniation Based on MRI Images

Zheng

Wen

2021

Journal of Healthcare Engineering

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MRI was used to measure the changes in the angle of the facet joints of the lumbar spine and analyze the relationship between it and the herniated lumbar intervertebral disc. Analysis of the causes of lumbar disc herniation from the anatomy and morphology of the spine provides a basis for the early diagnosis and prevention of lumbar disc herniation. There is a certain correlation between the changes shown in MRI imaging of lumbar disc herniation and the TCM syndromes of lumbar intervertebral disc herniation. There is a correlation between the syndromes of lumbar disc herniation and the direct signs of MRI: pathological type, herniated position, and degree of herniation. Indirect signs with MR, nerve root compression and dural sac compression, are related. The MRI examination results can help syndrome differentiation to improve its accuracy to a certain extent. MRI has high sensitivity for the measurement of the angle of the facet joints of the lumbar spine and can be used to study the correlation between the changes of the facet joint angles and the herniated disc. Facet joint asymmetry is closely related to lateral lumbar disc herniation, which may be one of its pathogenesis factors. The herniated intervertebral disc is mostly on the sagittal side of the facet joint, and the facet joint angle on the side of the herniated disc is more sagittal. The asymmetry of the facet joints is not related to the central lumbar disc herniation, and the angle of the facet joints on both sides of the central lumbar disc herniation is partial sagittal.

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Section: Mri Study Of Lumbar Disc Herniationmentioning

confidence: 99%

The Clinical Diagnostic Value of Lumbar Intervertebral Disc Herniation Based on MRI Images

Zheng

Wen

2021

Journal of Healthcare Engineering

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“…For example, the performance value of one IVD classification system was 86.5%, and this was based on a sparse shape reconstruction from a statistical shape model [ 32 ]. Additionally, an accuracy of 92.78% was reported by a study that classified normal disks and disk bulge by using a program called IVD Descriptor [ 13 ]. Compared to the accuracies of these previous studies, our accuracies were roughly the same or slightly inferior.…”

Section: Discussionmentioning

confidence: 99%

“…A study [9] on texture features that were obtained from IVD MR images used three different classifiers (ie, the back-propagation neural network, k-nearest neighbor, and support vector machine classifiers) to classify normal disks and IVDs and achieved a maximum accuracy of 83.33%. Additionally, many other methods have been proposed to automatically diagnose IVD diseases based on MR images [10][11][12][13]. Most of these models are for sagittal MR images, and there are very few studies that have used axial lumbar MR images, which are even more important in real clinical scenarios to identify disk bulge and herniation [13].…”

Section: Introductionmentioning

confidence: 99%

Automatically Diagnosing Disk Bulge and Disk Herniation With Lumbar Magnetic Resonance Images by Using Deep Convolutional Neural Networks: Method Development Study

Pan¹,

Zhang²,

He³

et al. 2021

JMIR Med Inform

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Background Disk herniation and disk bulge are two common disorders of lumbar intervertebral disks (IVDs) that often result in numbness, pain in the lower limbs, and lower back pain. Magnetic resonance (MR) imaging is one of the most efficient techniques for detecting lumbar diseases and is widely used for making clinical diagnoses at hospitals. However, there is a lack of efficient tools for effectively interpreting massive amounts of MR images to meet the requirements of many radiologists. Objective The aim of this study was to present an automatic system for diagnosing disk bulge and herniation that saves time and can effectively and significantly reduce the workload of radiologists. Methods The diagnosis of lumbar vertebral disorders is highly dependent on medical images. Therefore, we chose the two most common diseases—disk bulge and herniation—as research subjects. This study is mainly about identifying the position of IVDs (lumbar vertebra [L] 1 to L2, L2-L3, L3-L4, L4-L5, and L5 to sacral vertebra [S] 1) by analyzing the geometrical relationship between sagittal and axial images and classifying axial lumbar disk MR images via deep convolutional neural networks. Results This system involved 4 steps. In the first step, it automatically located vertebral bodies (including the L1, L2, L3, L4, L5, and S1) in sagittal images by using the faster region-based convolutional neural network, and our fourfold cross-validation showed 100% accuracy. In the second step, it spontaneously identified the corresponding disk in each axial lumbar disk MR image with 100% accuracy. In the third step, the accuracy for automatically locating the intervertebral disk region of interest in axial MR images was 100%. In the fourth step, the 3-class classification (normal disk, disk bulge, and disk herniation) accuracies for the L1-L2, L2-L3, L3-L4, L4-L5, and L5-S1 IVDs were 92.7%, 84.4%, 92.1%, 90.4%, and 84.2%, respectively. Conclusions The automatic diagnosis system was successfully built, and it could classify images of normal disks, disk bulge, and disk herniation. This system provided a web-based test for interpreting lumbar disk MR images that could significantly improve diagnostic efficiency and standardized diagnosis reports. This system can also be used to detect other lumbar abnormalities and cervical spondylosis.

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“…Then the classifications of normal and degenerated disc are done using LSMD. Beulah et al 17 proposed an automatic diagnostic system to diagnose disc bulge from axial lumbar spine MRI. In this, EM-based segmentation is used to segment the IVD from the axial slice, then the features are extracted from the Region of Interest (ROI) using HOG and novel Intervertebral disc Descriptor (IdD).…”

Section: Related Workmentioning

confidence: 99%

Diagnosis of disc bulge and disc desiccation in lumbar MRI using concatenated shape and texture features with random forest classifier

Sundarsingh

Kesavan

2019

Int J Imaging Syst Tech

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Disc bulge and disc desiccation are the most common abnormalities occurring in the spine, which leads to severe low back pain. Despite computer-aided automatic abnormality diagnostic imaging systems are available still there is a need for betterment in diagnostic accuracy and in processing time. Image processing with combined imaging features like shape and texture has given better diagnostic ability when compared with processing with individual features. In the present study, the desiccated and bulged Intervertebral Discs (IVDs) are diagnosed automatically by combining shape features extracted using Histogram of Oriented Gradients (HOG) and texture feature extracted using novel Local Sub-Rhombus Binary Relation Pattern (LS-RBRP) techniques with Random Forest (RF) classifier. The performance analysis projects that the RF with HOG+LS-RBRP has an overall better accuracy of 94.7% when compared with HOG (87%) and LS-RBRP (90.2%) with RF classifier separately in categorizing the normal IVD, disc bulge and disc desiccation in the lumbar spine MRI. K E Y W O R D Sdisc bulge, disc desiccation, feature extraction, HOG, LS-RBRP, MRI, RF classifier

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Disc bulge diagnostic model in axial lumbar MR images using Intervertebral disc Descriptor (IdD)

Cited by 14 publications

References 17 publications

The Clinical Diagnostic Value of Lumbar Intervertebral Disc Herniation Based on MRI Images

The Clinical Diagnostic Value of Lumbar Intervertebral Disc Herniation Based on MRI Images

Automatically Diagnosing Disk Bulge and Disk Herniation With Lumbar Magnetic Resonance Images by Using Deep Convolutional Neural Networks: Method Development Study

Diagnosis of disc bulge and disc desiccation in lumbar MRI using concatenated shape and texture features with random forest classifier

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