Application of a Texture Appearance Model for Segmentation of Lung Nodules on Computed Tomography of the Chest
Introduction. Lung cancer is one of the most critical diseases globally, with more than 1.6 million new cases registered every year. Early detection of lung cancer is essential; therefore, particular attention should be paid to the development of effective diagnostic and therapeutic procedures. Comp...
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| Format: | Article |
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Saint Petersburg Electrotechnical University "LETI"
2022-06-01
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| Series: | Известия высших учебных заведений России: Радиоэлектроника |
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| Online Access: | https://re.eltech.ru/jour/article/view/641 |
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| author | F. Shariaty V. A. Pavlov S. V. Zavjalov M. Orooji T. M. Pervunina |
| author_facet | F. Shariaty V. A. Pavlov S. V. Zavjalov M. Orooji T. M. Pervunina |
| author_sort | F. Shariaty |
| collection | DOAJ |
| description | Introduction. Lung cancer is one of the most critical diseases globally, with more than 1.6 million new cases registered every year. Early detection of lung cancer is essential; therefore, particular attention should be paid to the development of effective diagnostic and therapeutic procedures. Computer processing of CT scans in the course of lung cancer diagnostics involves the following stages: medical image acquisition, pre-processing of medical images, segmentation, and false-positive reduction. Since segmentation is an essential stage in the process of medical image analysis, the development of novel segmentation approaches is attracting much research interest. Model-based segmentation approaches have recently gained in popularity, largely due to their potential to restore lost information.Aim. To apply a texture appearance model for the segmentation of pulmonary nodules on computed tomography of the chest.Materials and methods. A novel model-based Texture Appearance Model (TAM) is proposed for precise and effective segmentation of all sorts of nodule regions. We taught the TAM for segmentation of a lung nodule in lung CT images using a combination of extracted texture characteristics from CT scans and Texture Representation of Image (TRI).Results. The results of applying the described TAM method to normal and noisy CT images are presented and compared to those obtained using the Region Growing and Active Contour algorithms, as well as the combination of Active Contour and Watershed algorithms. The TAM was tested in 85 nodules from a dataset, yielding an average dice similarity coefficient (DSC) of 84.75 percent.Conclusion. A novel method for segmenting nodules in the lung, which is capable of segmenting all forms of nodules with excellent accuracy, is proposed. This model-based technique, when used with the active loop algorithm, can enhance accuracy and decrease false positives by selecting the initial mask. The precision, dice, accuracy, and specificity of lung nodule segmentation on a normal CT scan are 85.5, 85, 96, and 98, which levels are superior to those produced by the Active Contour, Region Growing and the combination of Active Contour and Watershed algorithms. |
| format | Article |
| id | doaj-art-e01609fc7ebb4c1f8111bf660b1d68c3 |
| institution | Matheson Library |
| issn | 1993-8985 2658-4794 |
| language | Russian |
| publishDate | 2022-06-01 |
| publisher | Saint Petersburg Electrotechnical University "LETI" |
| record_format | Article |
| series | Известия высших учебных заведений России: Радиоэлектроника |
| spelling | doaj-art-e01609fc7ebb4c1f8111bf660b1d68c32025-08-03T19:50:27ZrusSaint Petersburg Electrotechnical University "LETI"Известия высших учебных заведений России: Радиоэлектроника1993-89852658-47942022-06-012539611710.32603/1993-8985-2022-25-3-96-117454Application of a Texture Appearance Model for Segmentation of Lung Nodules on Computed Tomography of the ChestF. Shariaty0V. A. Pavlov1S. V. Zavjalov2M. Orooji3T. M. Pervunina4Peter the Great St. Petersburg Polytechnic UniversityPeter the Great St. Petersburg Polytechnic University; Almazov National Medical Research CentrePeter the Great St. Petersburg Polytechnic UniversityUniversity of CaliforniaAlmazov National Medical Research CentreIntroduction. Lung cancer is one of the most critical diseases globally, with more than 1.6 million new cases registered every year. Early detection of lung cancer is essential; therefore, particular attention should be paid to the development of effective diagnostic and therapeutic procedures. Computer processing of CT scans in the course of lung cancer diagnostics involves the following stages: medical image acquisition, pre-processing of medical images, segmentation, and false-positive reduction. Since segmentation is an essential stage in the process of medical image analysis, the development of novel segmentation approaches is attracting much research interest. Model-based segmentation approaches have recently gained in popularity, largely due to their potential to restore lost information.Aim. To apply a texture appearance model for the segmentation of pulmonary nodules on computed tomography of the chest.Materials and methods. A novel model-based Texture Appearance Model (TAM) is proposed for precise and effective segmentation of all sorts of nodule regions. We taught the TAM for segmentation of a lung nodule in lung CT images using a combination of extracted texture characteristics from CT scans and Texture Representation of Image (TRI).Results. The results of applying the described TAM method to normal and noisy CT images are presented and compared to those obtained using the Region Growing and Active Contour algorithms, as well as the combination of Active Contour and Watershed algorithms. The TAM was tested in 85 nodules from a dataset, yielding an average dice similarity coefficient (DSC) of 84.75 percent.Conclusion. A novel method for segmenting nodules in the lung, which is capable of segmenting all forms of nodules with excellent accuracy, is proposed. This model-based technique, when used with the active loop algorithm, can enhance accuracy and decrease false positives by selecting the initial mask. The precision, dice, accuracy, and specificity of lung nodule segmentation on a normal CT scan are 85.5, 85, 96, and 98, which levels are superior to those produced by the Active Contour, Region Growing and the combination of Active Contour and Watershed algorithms.https://re.eltech.ru/jour/article/view/641texture appearance model (tam)texture feature extractioncomputer-aided detection system (cads)computed tomography scan (ct)texture representation of image (tri) |
| spellingShingle | F. Shariaty V. A. Pavlov S. V. Zavjalov M. Orooji T. M. Pervunina Application of a Texture Appearance Model for Segmentation of Lung Nodules on Computed Tomography of the Chest Известия высших учебных заведений России: Радиоэлектроника texture appearance model (tam) texture feature extraction computer-aided detection system (cads) computed tomography scan (ct) texture representation of image (tri) |
| title | Application of a Texture Appearance Model for Segmentation of Lung Nodules on Computed Tomography of the Chest |
| title_full | Application of a Texture Appearance Model for Segmentation of Lung Nodules on Computed Tomography of the Chest |
| title_fullStr | Application of a Texture Appearance Model for Segmentation of Lung Nodules on Computed Tomography of the Chest |
| title_full_unstemmed | Application of a Texture Appearance Model for Segmentation of Lung Nodules on Computed Tomography of the Chest |
| title_short | Application of a Texture Appearance Model for Segmentation of Lung Nodules on Computed Tomography of the Chest |
| title_sort | application of a texture appearance model for segmentation of lung nodules on computed tomography of the chest |
| topic | texture appearance model (tam) texture feature extraction computer-aided detection system (cads) computed tomography scan (ct) texture representation of image (tri) |
| url | https://re.eltech.ru/jour/article/view/641 |
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