Gray-Level Co-Occurrence Matrix Uniformity Correction Algorithm in Positron Emission Tomographic Image: A Phantom Study
High uniformity of positron emission tomography (PET) images in the field of nuclear medicine is necessary to obtain excellent and stable data from the system. In this study, we aimed to apply and optimize a PET/magnetic resonance (MR) imaging system by approaching the gray-level co-occurrence matri...
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2025-01-01
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| author | Kyuseok Kim Youngjin Lee |
| author_facet | Kyuseok Kim Youngjin Lee |
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| description | High uniformity of positron emission tomography (PET) images in the field of nuclear medicine is necessary to obtain excellent and stable data from the system. In this study, we aimed to apply and optimize a PET/magnetic resonance (MR) imaging system by approaching the gray-level co-occurrence matrix (GLCM), which is known to be efficient in the uniformity correction of images. CAIPIRINHA Dixon-VIBE was used as an MR image acquisition pulse sequence for the fast and accurate attenuation correction of PET images, and the phantom was constructed by injecting NaCl and NaCl + NiSO<sub>4</sub> solutions. The lambda value of the GLCM algorithm for uniformity correction of the acquired PET images was optimized in terms of energy and contrast. By applying the GLCM algorithm optimized in terms of energy and contrast to the PET images of phantoms using NaCl and NaCl + NiSO<sub>4</sub> solutions, average percent image uniformity (PIU) values of 26.01 and 83.76 were derived, respectively. Compared to the original PET image, an improved PIU value of more than 30% was derived from the PET image to which the proposed optimized GLCM algorithm was applied. In conclusion, we demonstrated that an algorithm optimized in terms of the GLCM energy and contrast can improve the uniformity of PET images. |
| format | Article |
| id | doaj-art-89df35d9d3f44865b19700e15dab8dc2 |
| institution | Kabale University |
| issn | 2304-6732 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
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| spelling | doaj-art-89df35d9d3f44865b19700e15dab8dc22025-01-24T13:46:16ZengMDPI AGPhotonics2304-67322025-01-011213310.3390/photonics12010033Gray-Level Co-Occurrence Matrix Uniformity Correction Algorithm in Positron Emission Tomographic Image: A Phantom StudyKyuseok Kim0Youngjin Lee1Department of Biomedical Engineering, Eulji University, 553, Sanseong-daero, Sujeong-gu, Seongnam-si 13135, Republic of KoreaDepartment of Radiological Science, Gachon University, 191, Hambakmoe-ro, Yeonsu-gu, Incheon 21936, Republic of KoreaHigh uniformity of positron emission tomography (PET) images in the field of nuclear medicine is necessary to obtain excellent and stable data from the system. In this study, we aimed to apply and optimize a PET/magnetic resonance (MR) imaging system by approaching the gray-level co-occurrence matrix (GLCM), which is known to be efficient in the uniformity correction of images. CAIPIRINHA Dixon-VIBE was used as an MR image acquisition pulse sequence for the fast and accurate attenuation correction of PET images, and the phantom was constructed by injecting NaCl and NaCl + NiSO<sub>4</sub> solutions. The lambda value of the GLCM algorithm for uniformity correction of the acquired PET images was optimized in terms of energy and contrast. By applying the GLCM algorithm optimized in terms of energy and contrast to the PET images of phantoms using NaCl and NaCl + NiSO<sub>4</sub> solutions, average percent image uniformity (PIU) values of 26.01 and 83.76 were derived, respectively. Compared to the original PET image, an improved PIU value of more than 30% was derived from the PET image to which the proposed optimized GLCM algorithm was applied. In conclusion, we demonstrated that an algorithm optimized in terms of the GLCM energy and contrast can improve the uniformity of PET images.https://www.mdpi.com/2304-6732/12/1/33positron emission tomography (PET)attenuation correctiongray-level co-occurrence matrix (GLCM)parameter optimizationuniformity correctionquantitative evaluation of image |
| spellingShingle | Kyuseok Kim Youngjin Lee Gray-Level Co-Occurrence Matrix Uniformity Correction Algorithm in Positron Emission Tomographic Image: A Phantom Study Photonics positron emission tomography (PET) attenuation correction gray-level co-occurrence matrix (GLCM) parameter optimization uniformity correction quantitative evaluation of image |
| title | Gray-Level Co-Occurrence Matrix Uniformity Correction Algorithm in Positron Emission Tomographic Image: A Phantom Study |
| title_full | Gray-Level Co-Occurrence Matrix Uniformity Correction Algorithm in Positron Emission Tomographic Image: A Phantom Study |
| title_fullStr | Gray-Level Co-Occurrence Matrix Uniformity Correction Algorithm in Positron Emission Tomographic Image: A Phantom Study |
| title_full_unstemmed | Gray-Level Co-Occurrence Matrix Uniformity Correction Algorithm in Positron Emission Tomographic Image: A Phantom Study |
| title_short | Gray-Level Co-Occurrence Matrix Uniformity Correction Algorithm in Positron Emission Tomographic Image: A Phantom Study |
| title_sort | gray level co occurrence matrix uniformity correction algorithm in positron emission tomographic image a phantom study |
| topic | positron emission tomography (PET) attenuation correction gray-level co-occurrence matrix (GLCM) parameter optimization uniformity correction quantitative evaluation of image |
| url | https://www.mdpi.com/2304-6732/12/1/33 |
| work_keys_str_mv | AT kyuseokkim graylevelcooccurrencematrixuniformitycorrectionalgorithminpositronemissiontomographicimageaphantomstudy AT youngjinlee graylevelcooccurrencematrixuniformitycorrectionalgorithminpositronemissiontomographicimageaphantomstudy |