Lumbar Spine Discs Labeling Using Axial View MRI Based on the Pixels Coordinate and Gray Level Features

Kafri, Ala S. Al; Sudirman, Sud; Hussain, Abir Jaafar; Fergus, Paul; Al‐Jumeily, Dhiya; Smadi, Hiba Al; Khalaf, Mohammed; Al-Jumaily, Mohammed; Al-Rashdan, Wasfi; Bashtawi, Mohammad; Mustafina, Jamila

doi:10.1007/978-3-319-63315-2_10

Cited by 6 publications

(3 citation statements)

References 20 publications

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“…The features used in these ten conventional algorithms are the raw pixel values and their corresponding locations. Pixel locations have been used in conjunction with pixel values to provide better spatial coherence to the segmentation result [36]. The results of the segmentation as measured using the four performance metrics are shown in Table II-V.…”

Section: Experiments Resultsmentioning

confidence: 99%

Segmentation of Lumbar Spine MRI Images for Stenosis Detection Using Patch-Based Pixel Classification Neural Network

Kafri

Sudirman

Hussain

et al. 2018

2018 IEEE Congress on Evolutionary Computation (CEC)

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This paper addresses the central problem of automatic segmentation of lumbar spine Magnetic Resonance Imaging (MRI) images to delineate boundaries between the anterior arch and posterior arch of the lumbar spine. This is necessary to efficiently detect the occurrence of lumbar spinal stenosis as a leading cause of Chronic Lower Back Pain. A patchbased classification neural network consisting of convolutional and fully connected layers is used to classify and label pixels in MRI images. The classifier is trained using overlapping patches of size 25x25 pixels taken from a set of cropped axial-view T2weighted MRI images of the bottom three intervertebral discs. A set of experiment is conducted to measure the performance of the classification network in segmenting the images when either all or each of the discs separately is used. Using pixel accuracy, mean accuracy, mean Intersection over Union (IoU), and frequency weighted IoU as the performance metrics we have shown that our approach produces better segmentation results than eleven other pixel classifiers. Furthermore, our experiment result also indicates that our approach produces more accurate delineation of all important boundaries and making it best suited for the subsequent stage of lumbar spinal stenosis detection.

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Section: Experiments Resultsmentioning

confidence: 99%

Segmentation of Lumbar Spine MRI Images for Stenosis Detection Using Patch-Based Pixel Classification Neural Network

Kafri

Sudirman

Hussain

et al. 2018

2018 IEEE Congress on Evolutionary Computation (CEC)

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“…Our model evaluation framework consists of training and testing diagnostics, supported by five important performance evaluations [10]. It is involved sensitivity (True positive) [26] , specificity (true negative), precision level, the F1 score point (F1), Youden's J statistic (J1), and accuracy calculated as illustrated in Equations 9 to 13. In addition, the models were characterised using ROC figures and the AUC figures, while the classification capability across operating method was determined.…”

Section: Methodsmentioning

confidence: 99%

A Data Science Methodology Based on Machine Learning Algorithms for Flood Severity Prediction

Khalaf

Hussain

Al‐Jumeily

et al. 2018

2018 IEEE Congress on Evolutionary Computation (CEC)

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In this paper, a novel application of machine learning algorithms including Neural Network architecture is presented for the prediction of flood severity. Floods are considered natural disasters that cause wide scale devastation to areas affected. The phenomenon of flooding is commonly caused by runoff from rivers and precipitation, specifically during periods of extremely high rainfall. Due to the concerns surrounding global warming and extreme ecological effects, flooding is considered a serious problem that has a negative impact on infrastructure and humankind. This paper attempts to address the issue of flood mitigation through the presentation of a new flood dataset, comprising 2000 annotated flood events, where the severity of the outcome is categorised according to 3 target classes, demonstrating the respective severities of floods. The paper also presents various types of machine learning algorithms for predicting flood severity and classifying outcomes into three classes, normal, abnormal, and high-risk floods. Extensive research indicates that artificial intelligence algorithms could produce enhancement when utilised for the pre-processing of flood data. These approaches helped in acquiring better accuracy in the classification techniques. Neural network architectures generally produce good outcomes in many applications, however, our experiments results illustrated that random forest classifier yields the optimal results in comparison with the benchmarked models.

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“…Al Kafri et al (2017) conducted a study to determine location of lumbar spinal disc in MR images based upon Pixels Coordinate and Gray Level Features. Highest weighting were obtained in using Weighted K-Nearest Neigbor (KNN) and Fine Gaussian Support Vector Machine (SVM) techniques [17].…”

Section: Figure 1 Regions Of Vertebral Column In a Mr Imagementioning

confidence: 99%

Enhancement on Enlarge Image for Identification Lumbar Radiculopathy at Magnetic Resonance Imaging

Yunus¹,

Harlan²,

Santony³

et al. 2020

TEM Journal

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Spinal nerve root pinched by herniated intervertebral disc will yield to painful complaints. Such condition in medical term is called as radiculopathy. The lesion usually occurs in the lower part of vertebral column, i.e. lumbar radiculopathy. To verify the diagnosis, the patient should undergo magnetic resonance imaging (MRI) examination. Further image processing is needed to aid the interpretation of these MRI images. The data in this study consisted of 40 MRI images, obtained by using General Electric Brivo MR 355 in T2-weighted sagittal relaxation phase, which are to be processed later by enlarged and enhancement processing. The image processing produced larger and clearer images, making it easier to observe lumbar radiculopathy. This method serves as a new breakthrough for the health providers in the management of lumbar radiculopathy patients.

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Lumbar Spine Discs Labeling Using Axial View MRI Based on the Pixels Coordinate and Gray Level Features

Cited by 6 publications

References 20 publications

Segmentation of Lumbar Spine MRI Images for Stenosis Detection Using Patch-Based Pixel Classification Neural Network

Segmentation of Lumbar Spine MRI Images for Stenosis Detection Using Patch-Based Pixel Classification Neural Network

A Data Science Methodology Based on Machine Learning Algorithms for Flood Severity Prediction

Enhancement on Enlarge Image for Identification Lumbar Radiculopathy at Magnetic Resonance Imaging

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