THE CONSTRUCTION OF THE FEATURE VECTOR IN THE DIAGNOSIS OF SARCOIDOSIS BASED ON THE FRACTAL ANALYSIS OF CT CHEST IMAGES
CT images corresponding to the cross-sections of the patients’ upper torso were analysed. The data set included the healthy class and 3 classes of cases affected by sarcoidosis. It was a state involving only the trachea – Sick(1), a state including trachea and lung parenchyma – Sick(2) and a state i...
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| Format: | Article |
| Language: | English |
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Lublin University of Technology
2019-06-01
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| Series: | Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska |
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| Online Access: | https://ph.pollub.pl/index.php/iapgos/article/view/743 |
| _version_ | 1828738890341548032 |
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| author | Zbigniew Omiotek Paweł Prokop |
| author_facet | Zbigniew Omiotek Paweł Prokop |
| author_sort | Zbigniew Omiotek |
| collection | DOAJ |
| description | CT images corresponding to the cross-sections of the patients’ upper torso were analysed. The data set included the healthy class and 3 classes of cases affected by sarcoidosis. It was a state involving only the trachea – Sick(1), a state including trachea and lung parenchyma – Sick(2) and a state involving only lung parenchyma – Sick(3). Based on a fractal analysis and a feature selection by linear stepwise regression, 4 descriptors were obtained, which were later used in the classification process. These were 2 fractal dimensions calculated by the variation and box counting methods, lacunarity calculated also with the box counting method and the intercept parameter calculated using the power spectral density method. Two descriptors were obtained as a result of a gray image analysis, and 2 more were the effect of a binary image analysis. The effectiveness of the descriptors was verified using 8 popular classification methods. In the process of classifier testing, the overall classification accuracy was 90.97%, and the healthy cases were detected with the accuracy of 100%. In turn, the accuracy of recognition of the sick cases was: Sick(1) – 92.50%, Sick(2) – 87.50% and Sick(3) – 90.00%. In the classification process, the best results were obtained with the support vector machine and the naive Bayes classifier. The results of the research have shown the high efficiency of a fractal analysis as a tool for the feature vector extraction in the computer aided diagnosis of sarcoidosis. |
| first_indexed | 2024-04-13T00:11:59Z |
| format | Article |
| id | doaj.art-f98b662116f34a2585e79258119bb1c8 |
| institution | Directory Open Access Journal |
| issn | 2083-0157 2391-6761 |
| language | English |
| last_indexed | 2024-04-13T00:11:59Z |
| publishDate | 2019-06-01 |
| publisher | Lublin University of Technology |
| record_format | Article |
| series | Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska |
| spelling | doaj.art-f98b662116f34a2585e79258119bb1c82022-12-22T03:11:05ZengLublin University of TechnologyInformatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska2083-01572391-67612019-06-019210.5604/01.3001.0013.2541THE CONSTRUCTION OF THE FEATURE VECTOR IN THE DIAGNOSIS OF SARCOIDOSIS BASED ON THE FRACTAL ANALYSIS OF CT CHEST IMAGESZbigniew Omiotek0Paweł Prokop1Lublin Univeristy of Technology, Institute of Electronics and Computer ScienceLublin University of TechnologyCT images corresponding to the cross-sections of the patients’ upper torso were analysed. The data set included the healthy class and 3 classes of cases affected by sarcoidosis. It was a state involving only the trachea – Sick(1), a state including trachea and lung parenchyma – Sick(2) and a state involving only lung parenchyma – Sick(3). Based on a fractal analysis and a feature selection by linear stepwise regression, 4 descriptors were obtained, which were later used in the classification process. These were 2 fractal dimensions calculated by the variation and box counting methods, lacunarity calculated also with the box counting method and the intercept parameter calculated using the power spectral density method. Two descriptors were obtained as a result of a gray image analysis, and 2 more were the effect of a binary image analysis. The effectiveness of the descriptors was verified using 8 popular classification methods. In the process of classifier testing, the overall classification accuracy was 90.97%, and the healthy cases were detected with the accuracy of 100%. In turn, the accuracy of recognition of the sick cases was: Sick(1) – 92.50%, Sick(2) – 87.50% and Sick(3) – 90.00%. In the classification process, the best results were obtained with the support vector machine and the naive Bayes classifier. The results of the research have shown the high efficiency of a fractal analysis as a tool for the feature vector extraction in the computer aided diagnosis of sarcoidosis.https://ph.pollub.pl/index.php/iapgos/article/view/743fractalssarcoidosiscomputed tomographyimage texture analysis |
| spellingShingle | Zbigniew Omiotek Paweł Prokop THE CONSTRUCTION OF THE FEATURE VECTOR IN THE DIAGNOSIS OF SARCOIDOSIS BASED ON THE FRACTAL ANALYSIS OF CT CHEST IMAGES Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska fractals sarcoidosis computed tomography image texture analysis |
| title | THE CONSTRUCTION OF THE FEATURE VECTOR IN THE DIAGNOSIS OF SARCOIDOSIS BASED ON THE FRACTAL ANALYSIS OF CT CHEST IMAGES |
| title_full | THE CONSTRUCTION OF THE FEATURE VECTOR IN THE DIAGNOSIS OF SARCOIDOSIS BASED ON THE FRACTAL ANALYSIS OF CT CHEST IMAGES |
| title_fullStr | THE CONSTRUCTION OF THE FEATURE VECTOR IN THE DIAGNOSIS OF SARCOIDOSIS BASED ON THE FRACTAL ANALYSIS OF CT CHEST IMAGES |
| title_full_unstemmed | THE CONSTRUCTION OF THE FEATURE VECTOR IN THE DIAGNOSIS OF SARCOIDOSIS BASED ON THE FRACTAL ANALYSIS OF CT CHEST IMAGES |
| title_short | THE CONSTRUCTION OF THE FEATURE VECTOR IN THE DIAGNOSIS OF SARCOIDOSIS BASED ON THE FRACTAL ANALYSIS OF CT CHEST IMAGES |
| title_sort | construction of the feature vector in the diagnosis of sarcoidosis based on the fractal analysis of ct chest images |
| topic | fractals sarcoidosis computed tomography image texture analysis |
| url | https://ph.pollub.pl/index.php/iapgos/article/view/743 |
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