Calculation of sharpness in lung images of pleural effusion patients and normal lung images using the thresholding segmentation method
<p>Research on the calculation of tapering from lung's images of patients with pleural effusion and normal lungs has been carrying out using the thresholding segmentation method. The tapering calculation was done using the Matlab programming language by applying the thresholding segmentat...
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| Format: | Article |
| Language: | English |
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Universitas Sultan Ageng Tirtayasa
2020-08-01
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| Series: | Gravity: Jurnal Ilmiah Penelitian dan Pembelajaran Fisika |
| Subjects: | |
| Online Access: | http://jurnal.untirta.ac.id/index.php/Gravity/article/view/8384 |
| _version_ | 1819011417455460352 |
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| author | Indah Nurhidayati Sinta M Siagian |
| author_facet | Indah Nurhidayati Sinta M Siagian |
| author_sort | Indah Nurhidayati |
| collection | DOAJ |
| description | <p>Research on the calculation of tapering from lung's images of patients with pleural effusion and normal lungs has been carrying out using the thresholding segmentation method. The tapering calculation was done using the Matlab programming language by applying the thresholding segmentation method's image processing theory. Images sharpness was obtaining from calculating the longest distance from all distances that were searching in the program. The steps taken in this research were image quality improvement, determination of the region of interest (ROI), thresholding segmentation, and calculating the tilt. Taper count was performing on eight lung images identified pleural effusion and eight lung images identified as normal. In 8 images of lungs pleural effusions, each taper was obtained 166; 159; 167; 167; 150; 157; 114; and 149. Whereas in 8 images of normal lungs, it was obtained that the respective curls were 187; 174; 181; 198; 199; 195; 179; and 195. The analysis showed that the lung's images of pleural effusion patients had a tapering of less than 171. In contrast, normal lung images had a tapering of more than 171, so that one characteristic was obtained that could distinguish between normal lungs and pleural effusions. It can facilitate medical personnel in the early detection of pleural effusion patients so that they can be handled quickly and accurately.</p> |
| first_indexed | 2024-12-21T01:27:50Z |
| format | Article |
| id | doaj.art-fad62e12c8c94c318726673177d76582 |
| institution | Directory Open Access Journal |
| issn | 2442-515X 2528-1976 |
| language | English |
| last_indexed | 2024-12-21T01:27:50Z |
| publishDate | 2020-08-01 |
| publisher | Universitas Sultan Ageng Tirtayasa |
| record_format | Article |
| series | Gravity: Jurnal Ilmiah Penelitian dan Pembelajaran Fisika |
| spelling | doaj.art-fad62e12c8c94c318726673177d765822022-12-21T19:20:27ZengUniversitas Sultan Ageng TirtayasaGravity: Jurnal Ilmiah Penelitian dan Pembelajaran Fisika2442-515X2528-19762020-08-016210.30870/gravity.v6i2.83845811Calculation of sharpness in lung images of pleural effusion patients and normal lung images using the thresholding segmentation methodIndah Nurhidayati0Sinta M Siagian1Department of Physics, Institut Teknologi dan Sains Nahdlatul Ulama PekalonganDepartment of Electrical Engineering, Politeknik Negeri Medan<p>Research on the calculation of tapering from lung's images of patients with pleural effusion and normal lungs has been carrying out using the thresholding segmentation method. The tapering calculation was done using the Matlab programming language by applying the thresholding segmentation method's image processing theory. Images sharpness was obtaining from calculating the longest distance from all distances that were searching in the program. The steps taken in this research were image quality improvement, determination of the region of interest (ROI), thresholding segmentation, and calculating the tilt. Taper count was performing on eight lung images identified pleural effusion and eight lung images identified as normal. In 8 images of lungs pleural effusions, each taper was obtained 166; 159; 167; 167; 150; 157; 114; and 149. Whereas in 8 images of normal lungs, it was obtained that the respective curls were 187; 174; 181; 198; 199; 195; 179; and 195. The analysis showed that the lung's images of pleural effusion patients had a tapering of less than 171. In contrast, normal lung images had a tapering of more than 171, so that one characteristic was obtained that could distinguish between normal lungs and pleural effusions. It can facilitate medical personnel in the early detection of pleural effusion patients so that they can be handled quickly and accurately.</p>http://jurnal.untirta.ac.id/index.php/Gravity/article/view/8384pleural effusiontaperingthresholding segmentation |
| spellingShingle | Indah Nurhidayati Sinta M Siagian Calculation of sharpness in lung images of pleural effusion patients and normal lung images using the thresholding segmentation method Gravity: Jurnal Ilmiah Penelitian dan Pembelajaran Fisika pleural effusion tapering thresholding segmentation |
| title | Calculation of sharpness in lung images of pleural effusion patients and normal lung images using the thresholding segmentation method |
| title_full | Calculation of sharpness in lung images of pleural effusion patients and normal lung images using the thresholding segmentation method |
| title_fullStr | Calculation of sharpness in lung images of pleural effusion patients and normal lung images using the thresholding segmentation method |
| title_full_unstemmed | Calculation of sharpness in lung images of pleural effusion patients and normal lung images using the thresholding segmentation method |
| title_short | Calculation of sharpness in lung images of pleural effusion patients and normal lung images using the thresholding segmentation method |
| title_sort | calculation of sharpness in lung images of pleural effusion patients and normal lung images using the thresholding segmentation method |
| topic | pleural effusion tapering thresholding segmentation |
| url | http://jurnal.untirta.ac.id/index.php/Gravity/article/view/8384 |
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