Real-Time Automatic Configuration of Brain MRI: A Comparative Study of SIFT Descriptors and YOLO Neural Network
This work presents two approaches to image processing in brain magnetic resonance imaging (MRI) to enhance slice planning during examinations. The first approach involves capturing images from the operator’s console during slice planning for two different brain examinations. From these images, Scale...
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MDPI AG
2024-12-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/1/147 |
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| author | Rávison Amaral Almeida Júlio César Porto de Carvalho Antônio Wilson Vieira Heveraldo Rodrigues de Oliveira Marcos F. S. V. D’Angelo |
| author_facet | Rávison Amaral Almeida Júlio César Porto de Carvalho Antônio Wilson Vieira Heveraldo Rodrigues de Oliveira Marcos F. S. V. D’Angelo |
| author_sort | Rávison Amaral Almeida |
| collection | DOAJ |
| description | This work presents two approaches to image processing in brain magnetic resonance imaging (MRI) to enhance slice planning during examinations. The first approach involves capturing images from the operator’s console during slice planning for two different brain examinations. From these images, Scale-Invariant Feature Transform (SIFT) descriptors are extracted from the regions of interest. These descriptors are then utilized to train and test a model for image matching. The second approach introduces a novel method based on the YOLO (You Only Look Once) neural network, which is designed to automatically align and orient cutting planes. Both methods aim to automate and assist operators in decision making during MRI slice planning, thereby reducing human dependency and improving examination accuracy. The SIFT-based method demonstrated satisfactory results, meeting the necessary requirements for accurate brain examinations. Meanwhile, the YOLO-based method provides a more advanced and automated solution to detect and align structures in brain MRI images. These two distinct approaches are intended to be compared, highlighting their respective strengths and weaknesses in the context of brain MRI slice planning. |
| format | Article |
| id | doaj-art-359b30add3f84e5cadbc0fa79f72c1ee |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-359b30add3f84e5cadbc0fa79f72c1ee2025-01-10T13:14:36ZengMDPI AGApplied Sciences2076-34172024-12-0115114710.3390/app15010147Real-Time Automatic Configuration of Brain MRI: A Comparative Study of SIFT Descriptors and YOLO Neural NetworkRávison Amaral Almeida0Júlio César Porto de Carvalho1Antônio Wilson Vieira2Heveraldo Rodrigues de Oliveira3Marcos F. S. V. D’Angelo4Graduate Program in Computer Modeling and Systems, UNIMONTES-State University of Montes Claros, Av. Rui Braga, sn, Vila Mauricéia, Montes Claros 39401-089, BrazilGraduate Program in Computer Modeling and Systems, UNIMONTES-State University of Montes Claros, Av. Rui Braga, sn, Vila Mauricéia, Montes Claros 39401-089, BrazilDepartment of Exact Sciences, UNIMONTES-State University of Montes Claros, Av. Rui Braga, sn, Vila Mauricéia, Montes Claros 39401-089, BrazilDepartment of Computer Science, UNIMONTES-State University of Montes Claros, Av. Rui Braga, sn, Vila Mauricéia, Montes Claros 39401-089, BrazilDepartment of Computer Science, UNIMONTES-State University of Montes Claros, Av. Rui Braga, sn, Vila Mauricéia, Montes Claros 39401-089, BrazilThis work presents two approaches to image processing in brain magnetic resonance imaging (MRI) to enhance slice planning during examinations. The first approach involves capturing images from the operator’s console during slice planning for two different brain examinations. From these images, Scale-Invariant Feature Transform (SIFT) descriptors are extracted from the regions of interest. These descriptors are then utilized to train and test a model for image matching. The second approach introduces a novel method based on the YOLO (You Only Look Once) neural network, which is designed to automatically align and orient cutting planes. Both methods aim to automate and assist operators in decision making during MRI slice planning, thereby reducing human dependency and improving examination accuracy. The SIFT-based method demonstrated satisfactory results, meeting the necessary requirements for accurate brain examinations. Meanwhile, the YOLO-based method provides a more advanced and automated solution to detect and align structures in brain MRI images. These two distinct approaches are intended to be compared, highlighting their respective strengths and weaknesses in the context of brain MRI slice planning.https://www.mdpi.com/2076-3417/15/1/147magnetic resonance imagingdeep learningcomputer vision |
| spellingShingle | Rávison Amaral Almeida Júlio César Porto de Carvalho Antônio Wilson Vieira Heveraldo Rodrigues de Oliveira Marcos F. S. V. D’Angelo Real-Time Automatic Configuration of Brain MRI: A Comparative Study of SIFT Descriptors and YOLO Neural Network Applied Sciences magnetic resonance imaging deep learning computer vision |
| title | Real-Time Automatic Configuration of Brain MRI: A Comparative Study of SIFT Descriptors and YOLO Neural Network |
| title_full | Real-Time Automatic Configuration of Brain MRI: A Comparative Study of SIFT Descriptors and YOLO Neural Network |
| title_fullStr | Real-Time Automatic Configuration of Brain MRI: A Comparative Study of SIFT Descriptors and YOLO Neural Network |
| title_full_unstemmed | Real-Time Automatic Configuration of Brain MRI: A Comparative Study of SIFT Descriptors and YOLO Neural Network |
| title_short | Real-Time Automatic Configuration of Brain MRI: A Comparative Study of SIFT Descriptors and YOLO Neural Network |
| title_sort | real time automatic configuration of brain mri a comparative study of sift descriptors and yolo neural network |
| topic | magnetic resonance imaging deep learning computer vision |
| url | https://www.mdpi.com/2076-3417/15/1/147 |
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