Semantic segmentation and PSO based method for segmenting liver and lesion from CT images
The liver is a vital organ of the human body and hepatic cancer is one of the major causes of cancer deaths. Early and rapid diagnosis can reduce the mortality rate. It can be achieved through computerized cancer diagnosis and surgery planning systems. Segmentation plays a major role in these system...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Polish Academy of Sciences
2022-09-01
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| Series: | International Journal of Electronics and Telecommunications |
| Subjects: | |
| Online Access: | https://journals.pan.pl/Content/124275/PDF/23-3511-12099-1-PB.pdf |
| _version_ | 1828379774491295744 |
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| author | P Vaidehi Nayantara Surekha Kamath Manjunath KN Rajagopal Kadavigere |
| author_facet | P Vaidehi Nayantara Surekha Kamath Manjunath KN Rajagopal Kadavigere |
| author_sort | P Vaidehi Nayantara |
| collection | DOAJ |
| description | The liver is a vital organ of the human body and hepatic cancer is one of the major causes of cancer deaths. Early and rapid diagnosis can reduce the mortality rate. It can be achieved through computerized cancer diagnosis and surgery planning systems. Segmentation plays a major role in these systems. This work evaluated the efficacy of the SegNet model in liver and particle swarm optimization-based clustering technique in liver lesion segmentation. Over 2400 CT images were used for training the deep learning network and ten CT datasets for validating the algorithm. The segmentation results were satisfactory. The values for Dice Coefficient and volumetric overlap error achieved were 0.940 ± 0.022 and 0.112 ± 0.038, respectively for liver and the results for lesion delineation were 0.4629 ± 0.287 and 0.6986 ± 0.203, respectively. The proposed method is effective for liver segmentation. However, lesion segmentation needs to be further improved for better accuracy. |
| first_indexed | 2024-12-10T03:47:01Z |
| format | Article |
| id | doaj.art-a62b2072c48b4b7981ccd9522547b421 |
| institution | Directory Open Access Journal |
| issn | 2081-8491 2300-1933 |
| language | English |
| last_indexed | 2024-12-10T03:47:01Z |
| publishDate | 2022-09-01 |
| publisher | Polish Academy of Sciences |
| record_format | Article |
| series | International Journal of Electronics and Telecommunications |
| spelling | doaj.art-a62b2072c48b4b7981ccd9522547b4212022-12-22T02:03:25ZengPolish Academy of SciencesInternational Journal of Electronics and Telecommunications2081-84912300-19332022-09-01vol. 68No 3635640https://doi.org/10.24425/ijet.2022.141283Semantic segmentation and PSO based method for segmenting liver and lesion from CT imagesP Vaidehi Nayantara0Surekha Kamath1Manjunath KN2Rajagopal Kadavigere3Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, IndiaDepartment of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, IndiaDepartment of Computer Science and Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, IndiaDepartment of Computer Science and Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, IndiaThe liver is a vital organ of the human body and hepatic cancer is one of the major causes of cancer deaths. Early and rapid diagnosis can reduce the mortality rate. It can be achieved through computerized cancer diagnosis and surgery planning systems. Segmentation plays a major role in these systems. This work evaluated the efficacy of the SegNet model in liver and particle swarm optimization-based clustering technique in liver lesion segmentation. Over 2400 CT images were used for training the deep learning network and ten CT datasets for validating the algorithm. The segmentation results were satisfactory. The values for Dice Coefficient and volumetric overlap error achieved were 0.940 ± 0.022 and 0.112 ± 0.038, respectively for liver and the results for lesion delineation were 0.4629 ± 0.287 and 0.6986 ± 0.203, respectively. The proposed method is effective for liver segmentation. However, lesion segmentation needs to be further improved for better accuracy.https://journals.pan.pl/Content/124275/PDF/23-3511-12099-1-PB.pdfliver lesion segmentationcomputed tomographysemantic segmentationsegnetparticle swarm optimization-based clusteringhounsfield unit |
| spellingShingle | P Vaidehi Nayantara Surekha Kamath Manjunath KN Rajagopal Kadavigere Semantic segmentation and PSO based method for segmenting liver and lesion from CT images International Journal of Electronics and Telecommunications liver lesion segmentation computed tomography semantic segmentation segnet particle swarm optimization-based clustering hounsfield unit |
| title | Semantic segmentation and PSO based method for segmenting liver and lesion from CT images |
| title_full | Semantic segmentation and PSO based method for segmenting liver and lesion from CT images |
| title_fullStr | Semantic segmentation and PSO based method for segmenting liver and lesion from CT images |
| title_full_unstemmed | Semantic segmentation and PSO based method for segmenting liver and lesion from CT images |
| title_short | Semantic segmentation and PSO based method for segmenting liver and lesion from CT images |
| title_sort | semantic segmentation and pso based method for segmenting liver and lesion from ct images |
| topic | liver lesion segmentation computed tomography semantic segmentation segnet particle swarm optimization-based clustering hounsfield unit |
| url | https://journals.pan.pl/Content/124275/PDF/23-3511-12099-1-PB.pdf |
| work_keys_str_mv | AT pvaidehinayantara semanticsegmentationandpsobasedmethodforsegmentingliverandlesionfromctimages AT surekhakamath semanticsegmentationandpsobasedmethodforsegmentingliverandlesionfromctimages AT manjunathkn semanticsegmentationandpsobasedmethodforsegmentingliverandlesionfromctimages AT rajagopalkadavigere semanticsegmentationandpsobasedmethodforsegmentingliverandlesionfromctimages |