Selective segmentation of brain abnormalities in colour MRI images using variational model.
| Title: | Selective segmentation of brain abnormalities in colour MRI images using variational model. |
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| Authors: | Azam, Akmal Shafiq Badarul, Jumaat, Abdul Kadir, Ibrahim, Shafaf, Azman, Nor Farihah, Zamalik, Sarah Farhana, Zakariah, Muhammad Zulkhairi |
| Source: | ESTEEM; Sep2024, Vol. 20, p117-134, 18p |
| Subject Terms: | BRAIN abnormalities, MAGNETIC resonance imaging, IMAGE segmentation, SURVIVAL rate, BRAIN imaging |
| Abstract: | Early detection of brain abnormalities is vital for enhancing patient outcomes and survival rates. However, accurately identifying and segmenting these abnormalities from MRI images remains a persistent challenge. This study assesses the efficacy of the Selective Local Image Fitting (SLIF) model in segmenting brain abnormalities from colour MRI images and compares its performance with converted greyscale counterparts. The rationale behind this comparison stems from standard practice in image segmentation, where colour images are often converted to greyscale before the segmentation task. Converting the image might degrade data by diminishing its dimensions, potentially affecting segmentation computations. This study intends to evaluate the influence of colour information on segmentation accuracy and efficiency by directly assessing the SLIF model on both colour and converted greyscale images. Segmentation accuracy was evaluated using metrics such as the Dice Similarity Coefficient (DSC), Matthews Correlation Coefficient (MCC), and Intersection-over-Union (IoU). Efficiency was determined by measuring the average elapsed processing time. Experimental results demonstrate that colour MRI brain images outperform their converted greyscale counterparts in segmentation accuracy, as colour providing essential supplementary information for precise abnormality delineation. Despite a slight increase in average elapsed processing time for colour images, the enhanced accuracy justifies this trade-off. These findings emphasize the importance of colour MRI in enhancing diagnostic accuracy, especially in detecting brain abnormalities. This study can be extended in future work to evaluate the segmentation accuracy and efficiency of brain abnormalities in 3D colour and greyscale MRI images. [ABSTRACT FROM AUTHOR] |
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| Database: | Complementary Index |
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| Items | – Name: Title Label: Title Group: Ti Data: Selective segmentation of brain abnormalities in colour MRI images using variational model. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Azam%2C+Akmal+Shafiq+Badarul%22">Azam, Akmal Shafiq Badarul</searchLink><br /><searchLink fieldCode="AR" term="%22Jumaat%2C+Abdul+Kadir%22">Jumaat, Abdul Kadir</searchLink><br /><searchLink fieldCode="AR" term="%22Ibrahim%2C+Shafaf%22">Ibrahim, Shafaf</searchLink><br /><searchLink fieldCode="AR" term="%22Azman%2C+Nor+Farihah%22">Azman, Nor Farihah</searchLink><br /><searchLink fieldCode="AR" term="%22Zamalik%2C+Sarah+Farhana%22">Zamalik, Sarah Farhana</searchLink><br /><searchLink fieldCode="AR" term="%22Zakariah%2C+Muhammad+Zulkhairi%22">Zakariah, Muhammad Zulkhairi</searchLink> – Name: TitleSource Label: Source Group: Src Data: ESTEEM; Sep2024, Vol. 20, p117-134, 18p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22BRAIN+abnormalities%22">BRAIN abnormalities</searchLink><br /><searchLink fieldCode="DE" term="%22MAGNETIC+resonance+imaging%22">MAGNETIC resonance imaging</searchLink><br /><searchLink fieldCode="DE" term="%22IMAGE+segmentation%22">IMAGE segmentation</searchLink><br /><searchLink fieldCode="DE" term="%22SURVIVAL+rate%22">SURVIVAL rate</searchLink><br /><searchLink fieldCode="DE" term="%22BRAIN+imaging%22">BRAIN imaging</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Early detection of brain abnormalities is vital for enhancing patient outcomes and survival rates. However, accurately identifying and segmenting these abnormalities from MRI images remains a persistent challenge. This study assesses the efficacy of the Selective Local Image Fitting (SLIF) model in segmenting brain abnormalities from colour MRI images and compares its performance with converted greyscale counterparts. The rationale behind this comparison stems from standard practice in image segmentation, where colour images are often converted to greyscale before the segmentation task. Converting the image might degrade data by diminishing its dimensions, potentially affecting segmentation computations. This study intends to evaluate the influence of colour information on segmentation accuracy and efficiency by directly assessing the SLIF model on both colour and converted greyscale images. Segmentation accuracy was evaluated using metrics such as the Dice Similarity Coefficient (DSC), Matthews Correlation Coefficient (MCC), and Intersection-over-Union (IoU). Efficiency was determined by measuring the average elapsed processing time. Experimental results demonstrate that colour MRI brain images outperform their converted greyscale counterparts in segmentation accuracy, as colour providing essential supplementary information for precise abnormality delineation. Despite a slight increase in average elapsed processing time for colour images, the enhanced accuracy justifies this trade-off. These findings emphasize the importance of colour MRI in enhancing diagnostic accuracy, especially in detecting brain abnormalities. This study can be extended in future work to evaluate the segmentation accuracy and efficiency of brain abnormalities in 3D colour and greyscale MRI images. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of ESTEEM is the property of Universiti Teknologi MARA, Pulau Pinang and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.24191/esteem.v20iSeptember.1854.g1820 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 18 StartPage: 117 Subjects: – SubjectFull: BRAIN abnormalities Type: general – SubjectFull: MAGNETIC resonance imaging Type: general – SubjectFull: IMAGE segmentation Type: general – SubjectFull: SURVIVAL rate Type: general – SubjectFull: BRAIN imaging Type: general Titles: – TitleFull: Selective segmentation of brain abnormalities in colour MRI images using variational model. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Azam, Akmal Shafiq Badarul – PersonEntity: Name: NameFull: Jumaat, Abdul Kadir – PersonEntity: Name: NameFull: Ibrahim, Shafaf – PersonEntity: Name: NameFull: Azman, Nor Farihah – PersonEntity: Name: NameFull: Zamalik, Sarah Farhana – PersonEntity: Name: NameFull: Zakariah, Muhammad Zulkhairi IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 09 Text: Sep2024 Type: published Y: 2024 Identifiers: – Type: issn-print Value: 16757939 Numbering: – Type: volume Value: 20 Titles: – TitleFull: ESTEEM Type: main |
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