Abnormalities detection in serial computed tomography brain images using multi-level segmentation approach
Segmentation, where pixels are categorized by tissue types, is essential in medical image processing. This paper proposes a multi-level Fuzzy C-Means method to extract an intracranial from its background and skull. Then, a two-level Otsu multi-thresholding method is applied to segment the intracrani...
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| Format: | Article |
| Language: | English |
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SPRINGER
2011
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| Online Access: | http://shdl.mmu.edu.my/1898/ http://shdl.mmu.edu.my/1898/1/4.pdf |
| _version_ | 1848789909769289728 |
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| author | Zaki, W. Mimi Diyana W. Fauzi, M. Faizal A. Besar, Rosli Ahmad, W. Siti Haimatul Munirah W. |
| author_facet | Zaki, W. Mimi Diyana W. Fauzi, M. Faizal A. Besar, Rosli Ahmad, W. Siti Haimatul Munirah W. |
| author_sort | Zaki, W. Mimi Diyana W. |
| building | MMU Institutional Repository |
| collection | Online Access |
| description | Segmentation, where pixels are categorized by tissue types, is essential in medical image processing. This paper proposes a multi-level Fuzzy C-Means method to extract an intracranial from its background and skull. Then, a two-level Otsu multi-thresholding method is applied to segment the intracranial structure into cerebrospinal fluid, brain matters and other homogenous regions. Based on symmetrical properties in the intracranial structures, the left-half and right-half segmented intracranial regions are quantitatively compared with respect to the intracranial midline. The segmented regions are found to be very useful in providing information regarding normal and abnormal structures in the intracranial because any asymmetry that is detected would indicate a high probability of abnormalities. Additionally, pixel intensity information such as standard deviation and the maximum value of the pixels of the segmented regions are used to distinguish abnormalities such as bleeding and calcification from normal cases. This experimental work uses a medical image database consisting of 519 normal and 201 abnormal serial computed tomography (CT) brain images from 31 patients. The proposed multi-level segmentation approach proved to effectively isolate important homogenous regions in CT brain images. The extracted features of the regions would provide a strong basis for the application of content-based medical image retrieval (CMBIR). |
| first_indexed | 2025-11-14T18:04:13Z |
| format | Article |
| id | mmu-1898 |
| institution | Multimedia University |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T18:04:13Z |
| publishDate | 2011 |
| publisher | SPRINGER |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | mmu-18982011-08-08T06:14:01Z http://shdl.mmu.edu.my/1898/ Abnormalities detection in serial computed tomography brain images using multi-level segmentation approach Zaki, W. Mimi Diyana W. Fauzi, M. Faizal A. Besar, Rosli Ahmad, W. Siti Haimatul Munirah W. QA75.5-76.95 Electronic computers. Computer science TA Engineering (General). Civil engineering (General) Segmentation, where pixels are categorized by tissue types, is essential in medical image processing. This paper proposes a multi-level Fuzzy C-Means method to extract an intracranial from its background and skull. Then, a two-level Otsu multi-thresholding method is applied to segment the intracranial structure into cerebrospinal fluid, brain matters and other homogenous regions. Based on symmetrical properties in the intracranial structures, the left-half and right-half segmented intracranial regions are quantitatively compared with respect to the intracranial midline. The segmented regions are found to be very useful in providing information regarding normal and abnormal structures in the intracranial because any asymmetry that is detected would indicate a high probability of abnormalities. Additionally, pixel intensity information such as standard deviation and the maximum value of the pixels of the segmented regions are used to distinguish abnormalities such as bleeding and calcification from normal cases. This experimental work uses a medical image database consisting of 519 normal and 201 abnormal serial computed tomography (CT) brain images from 31 patients. The proposed multi-level segmentation approach proved to effectively isolate important homogenous regions in CT brain images. The extracted features of the regions would provide a strong basis for the application of content-based medical image retrieval (CMBIR). SPRINGER 2011-08 Article NonPeerReviewed application/pdf en http://shdl.mmu.edu.my/1898/1/4.pdf Zaki, W. Mimi Diyana W. and Fauzi, M. Faizal A. and Besar, Rosli and Ahmad, W. Siti Haimatul Munirah W. (2011) Abnormalities detection in serial computed tomography brain images using multi-level segmentation approach. Multimedia Tools and Applications, 54 (2). pp. 321-340. ISSN 1380-7501 http://dx.doi.org/10.1007/s11042-010-0524-0 doi:10.1007/s11042-010-0524-0 doi:10.1007/s11042-010-0524-0 |
| spellingShingle | QA75.5-76.95 Electronic computers. Computer science TA Engineering (General). Civil engineering (General) Zaki, W. Mimi Diyana W. Fauzi, M. Faizal A. Besar, Rosli Ahmad, W. Siti Haimatul Munirah W. Abnormalities detection in serial computed tomography brain images using multi-level segmentation approach |
| title | Abnormalities detection in serial computed tomography brain images using multi-level segmentation approach |
| title_full | Abnormalities detection in serial computed tomography brain images using multi-level segmentation approach |
| title_fullStr | Abnormalities detection in serial computed tomography brain images using multi-level segmentation approach |
| title_full_unstemmed | Abnormalities detection in serial computed tomography brain images using multi-level segmentation approach |
| title_short | Abnormalities detection in serial computed tomography brain images using multi-level segmentation approach |
| title_sort | abnormalities detection in serial computed tomography brain images using multi-level segmentation approach |
| topic | QA75.5-76.95 Electronic computers. Computer science TA Engineering (General). Civil engineering (General) |
| url | http://shdl.mmu.edu.my/1898/ http://shdl.mmu.edu.my/1898/ http://shdl.mmu.edu.my/1898/ http://shdl.mmu.edu.my/1898/1/4.pdf |