Fast and Accurate Feature Extraction-Based Segmentation Framework for Spinal Cord Injury Severity Classification
Detection of spinal cord injury (SCI) is one of the major problems in MRI images to detect the affected portion of spinal cord regions using feature sets. Automatic detection of spinal cord atrophy is complex due to change in structure, size, and white matter. Delineating gray matter and white matte...
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| Format: | Article |
| Language: | English |
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IEEE
2019-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8688419/ |
| _version_ | 1819170076597682176 |
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| author | SK. Hasane Ahammad V. Rajesh MD. Zia Ur Rahman |
| author_facet | SK. Hasane Ahammad V. Rajesh MD. Zia Ur Rahman |
| author_sort | SK. Hasane Ahammad |
| collection | DOAJ |
| description | Detection of spinal cord injury (SCI) is one of the major problems in MRI images to detect the affected portion of spinal cord regions using feature sets. Automatic detection of spinal cord atrophy is complex due to change in structure, size, and white matter. Delineating gray matter and white matter are the essential factors that influence the detection of spinal cord atrophy and its severity. Automatic segmentation and classification are accurate methods for detecting the severity of the SCI. Hierarchical segmentation, partitioning segmentation, graph, and watershed segmentation methods are used to find the SCI segments in static fixed positions. Also, these segmentation models result in a high false positive rate due to over segmentation features and noise in the segmented regions. Furthermore, these classification methods fail to segment and detect the severity level in the affected region due to over segmentation. In order to overcome these issues, a novel segment-based classification model is required to find the severity of the injury and to predict the disease patterns on the over segmented regions and features. In the present model, a hybrid image threshold technique is used to segment the spinal cord regions for non-linear SVM classification approach. Among the traditional feature segmentation-based classification models, the proposed threshold-based non-linear SVM has better accuracy for SCI detection. The results proved that the present model is more efficient than the earlier approaches in terms of true positive rate (TP=0.9783) and accuracy (0.9683). |
| first_indexed | 2024-12-22T19:29:39Z |
| format | Article |
| id | doaj.art-dd87d75eabbe415e8add932c5ebd8e8e |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-22T19:29:39Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-dd87d75eabbe415e8add932c5ebd8e8e2022-12-21T18:15:08ZengIEEEIEEE Access2169-35362019-01-017460924610310.1109/ACCESS.2019.29095838688419Fast and Accurate Feature Extraction-Based Segmentation Framework for Spinal Cord Injury Severity ClassificationSK. Hasane Ahammad0V. Rajesh1MD. Zia Ur Rahman2https://orcid.org/0000-0002-4948-3870Department of Electronics and Communication Engineering, Koneru Lakshmaiah Education Foundation, Guntur, IndiaDepartment of Electronics and Communication Engineering, Koneru Lakshmaiah Education Foundation, Guntur, IndiaDepartment of Electronics and Communication Engineering, Koneru Lakshmaiah Education Foundation, Guntur, IndiaDetection of spinal cord injury (SCI) is one of the major problems in MRI images to detect the affected portion of spinal cord regions using feature sets. Automatic detection of spinal cord atrophy is complex due to change in structure, size, and white matter. Delineating gray matter and white matter are the essential factors that influence the detection of spinal cord atrophy and its severity. Automatic segmentation and classification are accurate methods for detecting the severity of the SCI. Hierarchical segmentation, partitioning segmentation, graph, and watershed segmentation methods are used to find the SCI segments in static fixed positions. Also, these segmentation models result in a high false positive rate due to over segmentation features and noise in the segmented regions. Furthermore, these classification methods fail to segment and detect the severity level in the affected region due to over segmentation. In order to overcome these issues, a novel segment-based classification model is required to find the severity of the injury and to predict the disease patterns on the over segmented regions and features. In the present model, a hybrid image threshold technique is used to segment the spinal cord regions for non-linear SVM classification approach. Among the traditional feature segmentation-based classification models, the proposed threshold-based non-linear SVM has better accuracy for SCI detection. The results proved that the present model is more efficient than the earlier approaches in terms of true positive rate (TP=0.9783) and accuracy (0.9683).https://ieeexplore.ieee.org/document/8688419/Machine learningspinal cord imagesupport vector machinesegmentation |
| spellingShingle | SK. Hasane Ahammad V. Rajesh MD. Zia Ur Rahman Fast and Accurate Feature Extraction-Based Segmentation Framework for Spinal Cord Injury Severity Classification IEEE Access Machine learning spinal cord image support vector machine segmentation |
| title | Fast and Accurate Feature Extraction-Based Segmentation Framework for Spinal Cord Injury Severity Classification |
| title_full | Fast and Accurate Feature Extraction-Based Segmentation Framework for Spinal Cord Injury Severity Classification |
| title_fullStr | Fast and Accurate Feature Extraction-Based Segmentation Framework for Spinal Cord Injury Severity Classification |
| title_full_unstemmed | Fast and Accurate Feature Extraction-Based Segmentation Framework for Spinal Cord Injury Severity Classification |
| title_short | Fast and Accurate Feature Extraction-Based Segmentation Framework for Spinal Cord Injury Severity Classification |
| title_sort | fast and accurate feature extraction based segmentation framework for spinal cord injury severity classification |
| topic | Machine learning spinal cord image support vector machine segmentation |
| url | https://ieeexplore.ieee.org/document/8688419/ |
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