Computer assisted characterization of cervical intervertebral disc degeneration in MRI

Michopoulou, Sofia; Boniatis, Ioannis; Costaridou, Lena; Cavouras, D.; Panagiotopoulos, Elias; Panayiotakis, George

doi:10.1088/1748-0221/4/05/p05022

Cited by 10 publications

(2 citation statements)

References 18 publications

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“…Tsai et al 15 proposed a B-spline Curve (boundary approximation technique) to mark the normal disc boundary and then convex and concave features are extracted to detect the ratio of the herniation in the IVD. Michopoulou et al 16 proposed a texture-based characterization method to identify the cervical IVD degeneration in which the features are extracted using grey level run-length matrix, GLCM and intensity histogram. Then the classifications of normal and degenerated disc are done using LSMD.…”

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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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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“…In the literature, most of the methods dealing with the automatic classification of IVDs according to their degree of degeneration, only consider healthy and unhealthy discs [11,12,13,14,15,16]. This separation corresponds to the difference between degrees I, II and III, IV, V in the Pfirrmann classification.…”

Section: Grade Imentioning

confidence: 99%

Intervertebral disc classification by its degree of degeneration from T2-weighted magnetic resonance images

et al. 2016

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Current Awareness in NMR in Biomedicine

2010

NMR in Biomedicine

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In order to keep subscribers up‐to‐date with the latest developments in their field, John Wiley & Sons are providing a current awareness service in each issue of the journal. The bibliography contains newly published material in the field of NMR in biomedicine. Each bibliography is divided into 12 sections: 1 Reviews; 2 General; 3 Technology; 4 Contrast Agents; 5 Brain and Nerves; 6 Neuropathology; 7 Cancer; 8 Cardiac, Vascular and Respiratory Systems; 9 Liver, Kidney and Other Organs; 10 Muscle and Orthopaedic; 11 Plants, Micro‐organisms and Parasites; 12 Others. Within each section, articles are listed in alphabetical order with respect to author. If, in the preceding period, no publications are located relevant to any one of these headings, that section will be omitted.

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Computer assisted characterization of cervical intervertebral disc degeneration in MRI

Cited by 10 publications

References 18 publications

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

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

Intervertebral disc classification by its degree of degeneration from T2-weighted magnetic resonance images

Current Awareness in NMR in Biomedicine

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