Localization of prostatic tumor’s infection based on normalized mutual information MRI image segmentation
To investigate the effect of normalized mutual information (MRI) image segmentation in accurate localization of prostate cancer with infection and the role in disease treatment, the normalized mutual information method is used to measure the similarity of images, so as to select the maps. Then, the...
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| Format: | Article |
| Language: | English |
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Elsevier
2021-03-01
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| Series: | Journal of Infection and Public Health |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1876034119302771 |
| _version_ | 1818827593387868160 |
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| author | Guoping Lu Lixin Zhou |
| author_facet | Guoping Lu Lixin Zhou |
| author_sort | Guoping Lu |
| collection | DOAJ |
| description | To investigate the effect of normalized mutual information (MRI) image segmentation in accurate localization of prostate cancer with infection and the role in disease treatment, the normalized mutual information method is used to measure the similarity of images, so as to select the maps. Then, the popular global weighted voting method and normalized mutual information method are applied to calculate the weights and carry out the label image fusion. The map selection method based on mutual information substantially completes the segmentation of the MRI image prostate. The prostate position is basically found on the 10 test images, and the positioning of the prostate organs is deviated in the worst case. In the case of poor multi-map segmentation, it usually happens when those are not well represented in the map. Because of the structural similarity of medical images, multi-atlas segmentation based on normalized mutual information method can be done. Using the prior information of atlas, the atlas label image can be selected. After fusion, the final segmentation of the test image can be completed, which has a high accuracy for the location of prostate cancer. This method can accurately delineate the target area in radiotherapy of prostate cancer and reduce the damage of rectum, bladder and other organs caused by radiotherapy. However, there are still some problems in this study, such as inadequate segmentation accuracy, long data processing time and so on. There is still a certain distance from practicality, and further research is needed. |
| first_indexed | 2024-12-19T00:46:01Z |
| format | Article |
| id | doaj.art-f21f58927e7744ad99c43baa26266cd3 |
| institution | Directory Open Access Journal |
| issn | 1876-0341 |
| language | English |
| last_indexed | 2024-12-19T00:46:01Z |
| publishDate | 2021-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Infection and Public Health |
| spelling | doaj.art-f21f58927e7744ad99c43baa26266cd32022-12-21T20:44:16ZengElsevierJournal of Infection and Public Health1876-03412021-03-01143432436Localization of prostatic tumor’s infection based on normalized mutual information MRI image segmentationGuoping Lu0Lixin Zhou1Department of Urology, Guangxi Minzu Hospital, Nanning, Guangxi 530001, China; Corresponding author.Department of Radiology, Guangxi Minzu Hospital, Nanning, Guangxi 530001, ChinaTo investigate the effect of normalized mutual information (MRI) image segmentation in accurate localization of prostate cancer with infection and the role in disease treatment, the normalized mutual information method is used to measure the similarity of images, so as to select the maps. Then, the popular global weighted voting method and normalized mutual information method are applied to calculate the weights and carry out the label image fusion. The map selection method based on mutual information substantially completes the segmentation of the MRI image prostate. The prostate position is basically found on the 10 test images, and the positioning of the prostate organs is deviated in the worst case. In the case of poor multi-map segmentation, it usually happens when those are not well represented in the map. Because of the structural similarity of medical images, multi-atlas segmentation based on normalized mutual information method can be done. Using the prior information of atlas, the atlas label image can be selected. After fusion, the final segmentation of the test image can be completed, which has a high accuracy for the location of prostate cancer. This method can accurately delineate the target area in radiotherapy of prostate cancer and reduce the damage of rectum, bladder and other organs caused by radiotherapy. However, there are still some problems in this study, such as inadequate segmentation accuracy, long data processing time and so on. There is still a certain distance from practicality, and further research is needed.http://www.sciencedirect.com/science/article/pii/S1876034119302771Normalized mutual informationMagnetic resonance imagingImage segmentationProstate cancerLocalization |
| spellingShingle | Guoping Lu Lixin Zhou Localization of prostatic tumor’s infection based on normalized mutual information MRI image segmentation Journal of Infection and Public Health Normalized mutual information Magnetic resonance imaging Image segmentation Prostate cancer Localization |
| title | Localization of prostatic tumor’s infection based on normalized mutual information MRI image segmentation |
| title_full | Localization of prostatic tumor’s infection based on normalized mutual information MRI image segmentation |
| title_fullStr | Localization of prostatic tumor’s infection based on normalized mutual information MRI image segmentation |
| title_full_unstemmed | Localization of prostatic tumor’s infection based on normalized mutual information MRI image segmentation |
| title_short | Localization of prostatic tumor’s infection based on normalized mutual information MRI image segmentation |
| title_sort | localization of prostatic tumor s infection based on normalized mutual information mri image segmentation |
| topic | Normalized mutual information Magnetic resonance imaging Image segmentation Prostate cancer Localization |
| url | http://www.sciencedirect.com/science/article/pii/S1876034119302771 |
| work_keys_str_mv | AT guopinglu localizationofprostatictumorsinfectionbasedonnormalizedmutualinformationmriimagesegmentation AT lixinzhou localizationofprostatictumorsinfectionbasedonnormalizedmutualinformationmriimagesegmentation |