A Novel Approach to Extract Exact Liver Image Boundary from Abdominal CT Scan using Neutrosophic Set and Fast Marching Method
Liver segmentation from abdominal computed tomography (CT) scan images is a complicated and challenging task. Due to the haziness in the liver pixel range, the neighboring organs of the liver have the same intensity level and existence of noise. Segmentation is necessary in the detection, identifica...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
De Gruyter
2019-09-01
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| Series: | Journal of Intelligent Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1515/jisys-2017-0144 |
| _version_ | 1818734748266135552 |
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| author | Siri Sangeeta K. Latte Mrityunjaya V. |
| author_facet | Siri Sangeeta K. Latte Mrityunjaya V. |
| author_sort | Siri Sangeeta K. |
| collection | DOAJ |
| description | Liver segmentation from abdominal computed tomography (CT) scan images is a complicated and challenging task. Due to the haziness in the liver pixel range, the neighboring organs of the liver have the same intensity level and existence of noise. Segmentation is necessary in the detection, identification, analysis, and measurement of objects in CT scan images. A novel approach is proposed to meet the challenges in extracting liver images from abdominal CT scan images. The proposed approach consists of three phases: (1) preprocessing, (2) CT scan image transformation to neutrosophic set, and (3) postprocessing. In preprocessing, noise in the CT scan is reduced by median filter. A “new structure” is introduced to transform a CT scan image into a neutrosophic domain, which is expressed using three membership subsets: true subset (T), false subset (F), and indeterminacy subset (I). This transform approximately extracts the liver structure. In the postprocessing phase, morphological operation is performed on the indeterminacy subset (I). A novel algorithm is designed to identify the start points within the liver section automatically. The fast marching method is applied at start points that grow outwardly to detect the accurate liver boundary. The evaluation of the proposed segmentation algorithm is concluded using area- and distance-based metrics. |
| first_indexed | 2024-12-18T00:10:17Z |
| format | Article |
| id | doaj.art-03b2072445aa4903973cd921d5a523f1 |
| institution | Directory Open Access Journal |
| issn | 0334-1860 2191-026X |
| language | English |
| last_indexed | 2024-12-18T00:10:17Z |
| publishDate | 2019-09-01 |
| publisher | De Gruyter |
| record_format | Article |
| series | Journal of Intelligent Systems |
| spelling | doaj.art-03b2072445aa4903973cd921d5a523f12022-12-21T21:27:41ZengDe GruyterJournal of Intelligent Systems0334-18602191-026X2019-09-0128451753210.1515/jisys-2017-0144A Novel Approach to Extract Exact Liver Image Boundary from Abdominal CT Scan using Neutrosophic Set and Fast Marching MethodSiri Sangeeta K.0Latte Mrityunjaya V.1Department of Electronics and Communication Engineering, Sapthagiri College of Engineering, Bengaluru, Karnataka, IndiaJSS Academy of Technical Education, Bengaluru, Karnataka, IndiaLiver segmentation from abdominal computed tomography (CT) scan images is a complicated and challenging task. Due to the haziness in the liver pixel range, the neighboring organs of the liver have the same intensity level and existence of noise. Segmentation is necessary in the detection, identification, analysis, and measurement of objects in CT scan images. A novel approach is proposed to meet the challenges in extracting liver images from abdominal CT scan images. The proposed approach consists of three phases: (1) preprocessing, (2) CT scan image transformation to neutrosophic set, and (3) postprocessing. In preprocessing, noise in the CT scan is reduced by median filter. A “new structure” is introduced to transform a CT scan image into a neutrosophic domain, which is expressed using three membership subsets: true subset (T), false subset (F), and indeterminacy subset (I). This transform approximately extracts the liver structure. In the postprocessing phase, morphological operation is performed on the indeterminacy subset (I). A novel algorithm is designed to identify the start points within the liver section automatically. The fast marching method is applied at start points that grow outwardly to detect the accurate liver boundary. The evaluation of the proposed segmentation algorithm is concluded using area- and distance-based metrics.https://doi.org/10.1515/jisys-2017-0144neutrosophic setfast marching methodcomputed tomographyimage processingliver segmentation |
| spellingShingle | Siri Sangeeta K. Latte Mrityunjaya V. A Novel Approach to Extract Exact Liver Image Boundary from Abdominal CT Scan using Neutrosophic Set and Fast Marching Method Journal of Intelligent Systems neutrosophic set fast marching method computed tomography image processing liver segmentation |
| title | A Novel Approach to Extract Exact Liver Image Boundary from Abdominal CT Scan using Neutrosophic Set and Fast Marching Method |
| title_full | A Novel Approach to Extract Exact Liver Image Boundary from Abdominal CT Scan using Neutrosophic Set and Fast Marching Method |
| title_fullStr | A Novel Approach to Extract Exact Liver Image Boundary from Abdominal CT Scan using Neutrosophic Set and Fast Marching Method |
| title_full_unstemmed | A Novel Approach to Extract Exact Liver Image Boundary from Abdominal CT Scan using Neutrosophic Set and Fast Marching Method |
| title_short | A Novel Approach to Extract Exact Liver Image Boundary from Abdominal CT Scan using Neutrosophic Set and Fast Marching Method |
| title_sort | novel approach to extract exact liver image boundary from abdominal ct scan using neutrosophic set and fast marching method |
| topic | neutrosophic set fast marching method computed tomography image processing liver segmentation |
| url | https://doi.org/10.1515/jisys-2017-0144 |
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