AN AI SEGMENTER ON MEDICAL IMAGING FOR GEOMATICS APPLICATIONS CONSISTING OF A TWO-STATE PIPELINE, SNNS NETWORK AND WATERSHED ALGORITHM
As is well known, image segmentation is widely used in the fields of echocardiography and diagnostic and interventional radiology. The delineation of structural components of various organs from 2D images is a technique used in the medical field in order to identify intervention targets with increas...
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2023-05-01
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| Series: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | https://isprs-archives.copernicus.org/articles/XLVIII-2-W3-2023/21/2023/isprs-archives-XLVIII-2-W3-2023-21-2023.pdf |
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| author | V. Barrile F. Cotroneo E. Genovese E. Barrile G. Bilotta |
| author_facet | V. Barrile F. Cotroneo E. Genovese E. Barrile G. Bilotta |
| author_sort | V. Barrile |
| collection | DOAJ |
| description | As is well known, image segmentation is widely used in the fields of echocardiography and diagnostic and interventional radiology. The delineation of structural components of various organs from 2D images is a technique used in the medical field in order to identify intervention targets with increasing precision and accuracy. In recent decades, the automation of this task has been the subject of intensive research. In particular, to improve the segmentation of such images, investigations have focused on the use of neural networks, and in particular convolutional neural networks (CNNs). However, most existing CNN-based methods can produce unsatisfactory segmentation masks without precise object boundaries (Wang, Chen, Ji, Fan & Ye Li, 2022); this is mainly due to the shadows and high noise in these images. To address the problem of automated image segmentation, this work proposes a pipeline technique with two stages (applied primarily to the echocardiographic domain): the first consisting of a Self-normalising Neural Networks (SNNs) performs image denoising, while the second applies a Watershed segmentation algorithm on the cleaned image. The latter is a technique successfully applied in geomatics and land surveying. The proposed methodology may be of interest both in the medical field and in the field of Geomatics where segmentation and classification operations are required in different application areas. |
| first_indexed | 2024-04-09T13:03:35Z |
| format | Article |
| id | doaj.art-609ae1aa40cc46eb85e8ee75886707b0 |
| institution | Directory Open Access Journal |
| issn | 1682-1750 2194-9034 |
| language | English |
| last_indexed | 2024-04-09T13:03:35Z |
| publishDate | 2023-05-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| spelling | doaj.art-609ae1aa40cc46eb85e8ee75886707b02023-05-12T16:30:11ZengCopernicus PublicationsThe International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences1682-17502194-90342023-05-01XLVIII-2-W3-2023212610.5194/isprs-archives-XLVIII-2-W3-2023-21-2023AN AI SEGMENTER ON MEDICAL IMAGING FOR GEOMATICS APPLICATIONS CONSISTING OF A TWO-STATE PIPELINE, SNNS NETWORK AND WATERSHED ALGORITHMV. Barrile0F. Cotroneo1E. Genovese2E. Barrile3G. Bilotta4Department for Civil, Energetic, Enviromental and Material Engineering, DICEAM, Geomatics Lab, Mediterranea University of Reggio Calabria, 89124 Reggio Calabria, ItalyDepartment for Civil, Energetic, Enviromental and Material Engineering, DICEAM, Geomatics Lab, Mediterranea University of Reggio Calabria, 89124 Reggio Calabria, ItalyDepartment for Civil, Energetic, Enviromental and Material Engineering, DICEAM, Geomatics Lab, Mediterranea University of Reggio Calabria, 89124 Reggio Calabria, ItalyVita-Salute San Raffaele University of Milan, ItalyDepartment for Civil, Energetic, Enviromental and Material Engineering, DICEAM, Geomatics Lab, Mediterranea University of Reggio Calabria, 89124 Reggio Calabria, ItalyAs is well known, image segmentation is widely used in the fields of echocardiography and diagnostic and interventional radiology. The delineation of structural components of various organs from 2D images is a technique used in the medical field in order to identify intervention targets with increasing precision and accuracy. In recent decades, the automation of this task has been the subject of intensive research. In particular, to improve the segmentation of such images, investigations have focused on the use of neural networks, and in particular convolutional neural networks (CNNs). However, most existing CNN-based methods can produce unsatisfactory segmentation masks without precise object boundaries (Wang, Chen, Ji, Fan & Ye Li, 2022); this is mainly due to the shadows and high noise in these images. To address the problem of automated image segmentation, this work proposes a pipeline technique with two stages (applied primarily to the echocardiographic domain): the first consisting of a Self-normalising Neural Networks (SNNs) performs image denoising, while the second applies a Watershed segmentation algorithm on the cleaned image. The latter is a technique successfully applied in geomatics and land surveying. The proposed methodology may be of interest both in the medical field and in the field of Geomatics where segmentation and classification operations are required in different application areas.https://isprs-archives.copernicus.org/articles/XLVIII-2-W3-2023/21/2023/isprs-archives-XLVIII-2-W3-2023-21-2023.pdf |
| spellingShingle | V. Barrile F. Cotroneo E. Genovese E. Barrile G. Bilotta AN AI SEGMENTER ON MEDICAL IMAGING FOR GEOMATICS APPLICATIONS CONSISTING OF A TWO-STATE PIPELINE, SNNS NETWORK AND WATERSHED ALGORITHM The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| title | AN AI SEGMENTER ON MEDICAL IMAGING FOR GEOMATICS APPLICATIONS CONSISTING OF A TWO-STATE PIPELINE, SNNS NETWORK AND WATERSHED ALGORITHM |
| title_full | AN AI SEGMENTER ON MEDICAL IMAGING FOR GEOMATICS APPLICATIONS CONSISTING OF A TWO-STATE PIPELINE, SNNS NETWORK AND WATERSHED ALGORITHM |
| title_fullStr | AN AI SEGMENTER ON MEDICAL IMAGING FOR GEOMATICS APPLICATIONS CONSISTING OF A TWO-STATE PIPELINE, SNNS NETWORK AND WATERSHED ALGORITHM |
| title_full_unstemmed | AN AI SEGMENTER ON MEDICAL IMAGING FOR GEOMATICS APPLICATIONS CONSISTING OF A TWO-STATE PIPELINE, SNNS NETWORK AND WATERSHED ALGORITHM |
| title_short | AN AI SEGMENTER ON MEDICAL IMAGING FOR GEOMATICS APPLICATIONS CONSISTING OF A TWO-STATE PIPELINE, SNNS NETWORK AND WATERSHED ALGORITHM |
| title_sort | ai segmenter on medical imaging for geomatics applications consisting of a two state pipeline snns network and watershed algorithm |
| url | https://isprs-archives.copernicus.org/articles/XLVIII-2-W3-2023/21/2023/isprs-archives-XLVIII-2-W3-2023-21-2023.pdf |
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