Computer aided diagnosis of degenerative intervertebral disc diseases from lumbar MR images

Oktay, Ayşe Betül; Albayrak, Nur Banu; Akgül, Yusuf Sinan

doi:10.1016/j.compmedimag.2014.04.006

Cited by 54 publications

(34 citation statements)

References 12 publications

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“…The classification accuracy of the proposed system is 92 %. In order to compare the unsupervised learned features with the hand-crafted features, popular feature types used in [1,9] are also implemented. The training is performed with six-fold-cross correlation and classification is performed via SVM.…”

Section: Methodsmentioning

confidence: 99%

“…We also implemented the state-of-the-art features used in the methods of [1,2,9] and compared them with the features learned with auto encoders.…”

Section: Introductionmentioning

confidence: 99%

“…Second, the computer-based evaluation of an MRI sequence would help the radiologists in decreasing the costs and speeding up the evaluation process. In the literature, many machine learning based approaches with hand-crafted features have been proposed for CAD of various intervertebral disc diseases from MR images [1,4,5,9].…”

Section: Introductionmentioning

confidence: 99%

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Diagnosis of Degenerative Intervertebral Disc Disease with Deep Networks and SVM

Oktay

Akgül

2016

Communications in Computer and Information Science

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Abstract. Computer aided diagnosis of degenerative intervertebral disc disease is a challenging task which has been targeted many times by computer vision and image processing community. This paper proposes a deep network approach for the diagnosis of degenerative intervertebral disc disease. Different from the classical deep networks, our system uses non-linear filters between the network layers that introduce domain dependent information into the network training for a faster training with lesser amount of data. The proposed system takes advantage of the unsupervised feature extraction with deep networks while requiring only a small amount of training data, which is a major problem for medical image analysis where obtaining large amounts of patient data is very difficult. The method is validated on a dataset containing 102 lumbar MR images. State-of-the-art hand-crafted feature extraction algorithms are compared with the unsupervisedly learned features and the proposed method outperforms the hand-crafted features.

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Section: Methodsmentioning

confidence: 99%

“…We also implemented the state-of-the-art features used in the methods of [1,2,9] and compared them with the features learned with auto encoders.…”

Section: Introductionmentioning

confidence: 99%

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Diagnosis of Degenerative Intervertebral Disc Disease with Deep Networks and SVM

Oktay

Akgül

2016

Communications in Computer and Information Science

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“…Disc bulge and disc desiccation are the common types of lumbar spine abnormality, which cause severe low back pain. 1 The spine consists of vertebrae and IVDs; in which the IVD located between the two spinal vertebrae consists of two layers namely: nucleus pulposus and annulus fibrosis. The annulus fibrosis is rich in collagen fibers.…”

Section: Introductionmentioning

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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“…Many researchers proposed different feature extraction method to describe different medical image characteristics. The method of general feature extraction can be divided into two classes: texture based features [8]- [12] and intensity based statistical features [13]- [17].…”

Section: Introductionmentioning

confidence: 99%

The Recognition of Basal Ganglia Area in the Brain MRI Image with Hybrid Classifier

Wu¹,

Chin²,

Chin³

et al. 2017

IJIEE

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Abstract-To understand the relationship between the volume of basal ganglia and parkinson's disease, this paper proposes the recognition of basal ganglia in the brain MRI image with hybrid classifier. It can locate the position of basal ganglia in the 2D brain MRI image. It employs three feature extraction methods to extract the texture feature of basal ganglia. The three feature extraction methods include GLCM, Law's mask and Hu's method .Next, we uses rank search method and genetic search method to perform feature selection. It can reduce computation complexity. Finally, hybrid classifier combining neuro-fuzzy system and adaboost algorithm is used to achieve recognition. From experimental result, we discovered that our proposed method has the high recognition rate.Index Terms-Parkinson's disease, magnetic resonance imaging, basal ganglia, feature pooling, neural-fuzzy-based adaboost.

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Computer aided diagnosis of degenerative intervertebral disc diseases from lumbar MR images

Cited by 54 publications

References 12 publications

Diagnosis of Degenerative Intervertebral Disc Disease with Deep Networks and SVM

Diagnosis of Degenerative Intervertebral Disc Disease with Deep Networks and SVM

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

The Recognition of Basal Ganglia Area in the Brain MRI Image with Hybrid Classifier

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