Al-Kafri, Ala S. and Sudirman, Sud and Hussain, Abir and Al-Jumeily, Dhiya and Fergus, Paul and Natalia, Friska and Meidia, Hira and Afriliana, Nunik and Sophian, Ali and Al-Jumaily, Mohammed and Al-Rashdan, Wasfi and Bashtawi, Mohammad (2018) Segmentation of Lumbar Spine MRI Images for Stenosis Detection Using Patch-Based Pixel Classification Neural Network. 2018 IEEE Congress on Evolutionary Computation (CEC).
Full text not available from this repository.Abstract
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 patch-based 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 25×25 pixels taken from a set of cropped axial-view T2-weighted 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.
| Item Type: | Article |
|---|---|
| Subjects: | 000 Computer Science, Information and General Works > 000 Computer Science, Knowledge and Systems > 005 Computer Programming 600 Technology (Applied Sciences) > 610 Medicine and Health > 617 Surgery, Regional Medicine, Dentistry, Ophthalmology, Otology, Audiology |
| Divisions: | Faculty of Engineering & Informatics > Informatics |
| Depositing User: | Administrator UMN Library |
| Date Deposited: | 11 Oct 2021 16:37 |
| Last Modified: | 11 Oct 2021 16:37 |
| URI: | https://kc.umn.ac.id/id/eprint/18657 |
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