Integrating Pixel-Based Algorithms for Area Measurement in Brain Tumor Classification
Diagnosing brain cancers in medicine necessitates an examination utilizing magnetic resonance imaging (MRI). picture processing techniques in the medical domain are integral to computed tomography detection in MRI due to their excellent picture fidelity and little radiation exposure. Nonetheless, th...
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| Format: | Article |
| Language: | English English |
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INTI International University
2025
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| Online Access: | http://eprints.intimal.edu.my/2141/ http://eprints.intimal.edu.my/2141/1/jods2025_02.pdf http://eprints.intimal.edu.my/2141/2/684 |
| _version_ | 1848766931387023360 |
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| author | Dwi Swasono, Rachmad |
| author_facet | Dwi Swasono, Rachmad |
| author_sort | Dwi Swasono, Rachmad |
| building | INTI Institutional Repository |
| collection | Online Access |
| description | Diagnosing brain cancers in medicine necessitates an examination utilizing magnetic resonance imaging (MRI). picture processing techniques in the medical domain are integral to computed tomography detection in MRI due to their excellent picture fidelity and little radiation exposure. Nonetheless, there remain deficiencies in the interpretation, analysis, and imaging of brain tumors in detection. This study seeks to identify brain tumors to ascertain their size and extent by a pixel-based methodology. The dataset utilized originates from Cipto Mangunkusumo Hospital in Jakarta and comprises T1 contrast and BMP sequences. The research procedure will employ many methodologies, including active contours, Otsu's method, and a combination of techniques, with comparisons utilizing the MRI MicroDicom viewer. The image testing phase utilizing Matlab and Python with thirteen image datasets. The findings from this study, which involved segmentation and extraction techniques to quantify the area of brain tumors using a pixel-based approach, indicate that the combined method outperforms alternative methods by achieving superior accuracy of 99%. Other methods fail to attain this level of accuracy, and the combined method also demonstrates optimal error differentiation in template matching. |
| first_indexed | 2025-11-14T11:58:59Z |
| format | Article |
| id | intimal-2141 |
| institution | INTI International University |
| institution_category | Local University |
| language | English English |
| last_indexed | 2025-11-14T11:58:59Z |
| publishDate | 2025 |
| publisher | INTI International University |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | intimal-21412025-06-19T08:45:23Z http://eprints.intimal.edu.my/2141/ Integrating Pixel-Based Algorithms for Area Measurement in Brain Tumor Classification Dwi Swasono, Rachmad QA75 Electronic computers. Computer science RB Pathology T Technology (General) TK Electrical engineering. Electronics Nuclear engineering Diagnosing brain cancers in medicine necessitates an examination utilizing magnetic resonance imaging (MRI). picture processing techniques in the medical domain are integral to computed tomography detection in MRI due to their excellent picture fidelity and little radiation exposure. Nonetheless, there remain deficiencies in the interpretation, analysis, and imaging of brain tumors in detection. This study seeks to identify brain tumors to ascertain their size and extent by a pixel-based methodology. The dataset utilized originates from Cipto Mangunkusumo Hospital in Jakarta and comprises T1 contrast and BMP sequences. The research procedure will employ many methodologies, including active contours, Otsu's method, and a combination of techniques, with comparisons utilizing the MRI MicroDicom viewer. The image testing phase utilizing Matlab and Python with thirteen image datasets. The findings from this study, which involved segmentation and extraction techniques to quantify the area of brain tumors using a pixel-based approach, indicate that the combined method outperforms alternative methods by achieving superior accuracy of 99%. Other methods fail to attain this level of accuracy, and the combined method also demonstrates optimal error differentiation in template matching. INTI International University 2025-06 Article PeerReviewed text en cc_by_4 http://eprints.intimal.edu.my/2141/1/jods2025_02.pdf text en cc_by_4 http://eprints.intimal.edu.my/2141/2/684 Dwi Swasono, Rachmad (2025) Integrating Pixel-Based Algorithms for Area Measurement in Brain Tumor Classification. Journal of Data Science, 2025 (2). pp. 1-13. ISSN 2805-5160 http://ipublishing.intimal.edu.my/jods.html |
| spellingShingle | QA75 Electronic computers. Computer science RB Pathology T Technology (General) TK Electrical engineering. Electronics Nuclear engineering Dwi Swasono, Rachmad Integrating Pixel-Based Algorithms for Area Measurement in Brain Tumor Classification |
| title | Integrating Pixel-Based Algorithms for Area Measurement in Brain Tumor Classification |
| title_full | Integrating Pixel-Based Algorithms for Area Measurement in Brain Tumor Classification |
| title_fullStr | Integrating Pixel-Based Algorithms for Area Measurement in Brain Tumor Classification |
| title_full_unstemmed | Integrating Pixel-Based Algorithms for Area Measurement in Brain Tumor Classification |
| title_short | Integrating Pixel-Based Algorithms for Area Measurement in Brain Tumor Classification |
| title_sort | integrating pixel-based algorithms for area measurement in brain tumor classification |
| topic | QA75 Electronic computers. Computer science RB Pathology T Technology (General) TK Electrical engineering. Electronics Nuclear engineering |
| url | http://eprints.intimal.edu.my/2141/ http://eprints.intimal.edu.my/2141/ http://eprints.intimal.edu.my/2141/1/jods2025_02.pdf http://eprints.intimal.edu.my/2141/2/684 |