Automated stroke lesion detection and diagnosis system
This study proposes a technique for automated detection and diagnosis of stroke lesions based on diffusion-weighted imaging (DWI). The technique consists of several stages which are pre-processing, segmentation, feature extraction, and classification. The proposed analytical framework of th...
| Main Authors: | , , , , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2017
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| Online Access: | http://psasir.upm.edu.my/id/eprint/60977/ http://psasir.upm.edu.my/id/eprint/60977/1/Automated%20stroke%20lesion%20detection%20and%20diagnosis%20system.pdf |
| _version_ | 1848854312736784384 |
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| author | Mohd Saad, N. M. Noor, N. S. Abdullah, A. R. Muda, Ahmad Sobri Muda, A. F. Abdul Rahman, N. N. S. |
| author_facet | Mohd Saad, N. M. Noor, N. S. Abdullah, A. R. Muda, Ahmad Sobri Muda, A. F. Abdul Rahman, N. N. S. |
| author_sort | Mohd Saad, N. |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | This study proposes a technique for automated detection and diagnosis of stroke lesions based on diffusion-weighted imaging (DWI). The technique consists of several stages which are pre-processing, segmentation, feature extraction, and classification. The proposed analytical framework of this study is based on Fuzzy C-Means (FCM) segmentation, statistical parameters for features extraction and rule-based classification. The three-dimensional (3D) view is developed to enable observing directions of the gained 3D structure along the three axes. The segmentation results have been validated by using Jaccard and Dice indices, false positive rate (FPR), and false negative rate (FNR). The results for Jaccard, Dice, FPR and FNR of acute stroke are 0.7, 0.84, 0.049 and 0.205, respectively. The accuracy for acute stroke is 90% and chronic stroke is 70%, while the sensitivity and the specificity is 84.38% and 83.33%, respectively. |
| first_indexed | 2025-11-15T11:07:52Z |
| format | Conference or Workshop Item |
| id | upm-60977 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T11:07:52Z |
| publishDate | 2017 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-609772019-05-14T03:12:16Z http://psasir.upm.edu.my/id/eprint/60977/ Automated stroke lesion detection and diagnosis system Mohd Saad, N. M. Noor, N. S. Abdullah, A. R. Muda, Ahmad Sobri Muda, A. F. Abdul Rahman, N. N. S. This study proposes a technique for automated detection and diagnosis of stroke lesions based on diffusion-weighted imaging (DWI). The technique consists of several stages which are pre-processing, segmentation, feature extraction, and classification. The proposed analytical framework of this study is based on Fuzzy C-Means (FCM) segmentation, statistical parameters for features extraction and rule-based classification. The three-dimensional (3D) view is developed to enable observing directions of the gained 3D structure along the three axes. The segmentation results have been validated by using Jaccard and Dice indices, false positive rate (FPR), and false negative rate (FNR). The results for Jaccard, Dice, FPR and FNR of acute stroke are 0.7, 0.84, 0.049 and 0.205, respectively. The accuracy for acute stroke is 90% and chronic stroke is 70%, while the sensitivity and the specificity is 84.38% and 83.33%, respectively. 2017 Conference or Workshop Item PeerReviewed text en http://psasir.upm.edu.my/id/eprint/60977/1/Automated%20stroke%20lesion%20detection%20and%20diagnosis%20system.pdf Mohd Saad, N. and M. Noor, N. S. and Abdullah, A. R. and Muda, Ahmad Sobri and Muda, A. F. and Abdul Rahman, N. N. S. (2017) Automated stroke lesion detection and diagnosis system. In: Proceedings of the International MultiConference of Engineers and Computer Scientists, 15-17 Mar. 2017, Hong Kong. (pp. 1-6). |
| spellingShingle | Mohd Saad, N. M. Noor, N. S. Abdullah, A. R. Muda, Ahmad Sobri Muda, A. F. Abdul Rahman, N. N. S. Automated stroke lesion detection and diagnosis system |
| title | Automated stroke lesion detection and diagnosis system |
| title_full | Automated stroke lesion detection and diagnosis system |
| title_fullStr | Automated stroke lesion detection and diagnosis system |
| title_full_unstemmed | Automated stroke lesion detection and diagnosis system |
| title_short | Automated stroke lesion detection and diagnosis system |
| title_sort | automated stroke lesion detection and diagnosis system |
| url | http://psasir.upm.edu.my/id/eprint/60977/ http://psasir.upm.edu.my/id/eprint/60977/1/Automated%20stroke%20lesion%20detection%20and%20diagnosis%20system.pdf |