Region growing segmentation of ultrasound images using gradients and local statistics
This paper describes a region growing segmentation algorithm for medical ultrasound images. The algorithm starts with anisotropic diffusion filtering to reduce speckle noise without blurring the edges. Then, region growing is performed starting from a seed point, using a merging criterion that compa...
- Autores:
- Tipo de recurso:
- Fecha de publicación:
- 2017
- Institución:
- Universidad Tecnológica de Bolívar
- Repositorio:
- Repositorio Institucional UTB
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.utb.edu.co:20.500.12585/8952
- Acceso en línea:
- https://hdl.handle.net/20.500.12585/8952
- Palabra clave:
- Anisotropic diffusion filtering
Medical ultrasound
Region growing segmentation
Anisotropy
Imaging systems
Medical imaging
Optical anisotropy
Tomography
Ultrasonic imaging
Anisotropic diffusion filtering
Intensity gradients
Medical ultrasound
Medical ultrasound images
Morphological closing
Region growing
Segmentation algorithms
Ultrasound image segmentation
Image segmentation
- Rights
- restrictedAccess
- License
- http://creativecommons.org/licenses/by-nc-nd/4.0/
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Region growing segmentation of ultrasound images using gradients and local statistics |
| title |
Region growing segmentation of ultrasound images using gradients and local statistics |
| spellingShingle |
Region growing segmentation of ultrasound images using gradients and local statistics Anisotropic diffusion filtering Medical ultrasound Region growing segmentation Anisotropy Imaging systems Medical imaging Optical anisotropy Tomography Ultrasonic imaging Anisotropic diffusion filtering Intensity gradients Medical ultrasound Medical ultrasound images Morphological closing Region growing Segmentation algorithms Ultrasound image segmentation Image segmentation |
| title_short |
Region growing segmentation of ultrasound images using gradients and local statistics |
| title_full |
Region growing segmentation of ultrasound images using gradients and local statistics |
| title_fullStr |
Region growing segmentation of ultrasound images using gradients and local statistics |
| title_full_unstemmed |
Region growing segmentation of ultrasound images using gradients and local statistics |
| title_sort |
Region growing segmentation of ultrasound images using gradients and local statistics |
| dc.contributor.editor.none.fl_str_mv |
Duric N. Heyde B. |
| dc.subject.keywords.none.fl_str_mv |
Anisotropic diffusion filtering Medical ultrasound Region growing segmentation Anisotropy Imaging systems Medical imaging Optical anisotropy Tomography Ultrasonic imaging Anisotropic diffusion filtering Intensity gradients Medical ultrasound Medical ultrasound images Morphological closing Region growing Segmentation algorithms Ultrasound image segmentation Image segmentation |
| topic |
Anisotropic diffusion filtering Medical ultrasound Region growing segmentation Anisotropy Imaging systems Medical imaging Optical anisotropy Tomography Ultrasonic imaging Anisotropic diffusion filtering Intensity gradients Medical ultrasound Medical ultrasound images Morphological closing Region growing Segmentation algorithms Ultrasound image segmentation Image segmentation |
| description |
This paper describes a region growing segmentation algorithm for medical ultrasound images. The algorithm starts with anisotropic diffusion filtering to reduce speckle noise without blurring the edges. Then, region growing is performed starting from a seed point, using a merging criterion that compares intensity gradients to the noise level inside the region. Finally, the boundaries are smoothed using morphological closing. The algorithm was evaluated with two simulated images and eleven phantom images and converged in 10 of them with accurate region delimitation. Preliminary results show that the proposed method can be used for ultrasound image segmentation and does not require previous knowledge of the anatomy of the structures. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only. |
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2017 |
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2017 |
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2020-03-26T16:32:39Z |
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2020-03-26T16:32:39Z |
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http://purl.org/coar/version/c_970fb48d4fbd8a85 |
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Conferencia |
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Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 10139 |
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9781510607231 |
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16057422 |
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https://hdl.handle.net/20.500.12585/8952 |
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10.1117/12.2254518 |
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Universidad Tecnológica de Bolívar |
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Repositorio UTB |
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57190165939 57190688459 57210822856 |
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Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 10139 9781510607231 16057422 10.1117/12.2254518 Universidad Tecnológica de Bolívar Repositorio UTB 57190165939 57190688459 57210822856 |
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https://hdl.handle.net/20.500.12585/8952 |
| dc.language.iso.none.fl_str_mv |
eng |
| language |
eng |
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15 February 2017 through 16 February 2017 |
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http://creativecommons.org/licenses/by-nc-nd/4.0/ |
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Atribución-NoComercial 4.0 Internacional |
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http://creativecommons.org/licenses/by-nc-nd/4.0/ Atribución-NoComercial 4.0 Internacional http://purl.org/coar/access_right/c_16ec |
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application/pdf |
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SPIE |
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SPIE |
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Duric N.Heyde B.Mercado-Aguirre I.M.Patiño Vanegas, AlbertoContreras Ortiz, Sonia Helena2020-03-26T16:32:39Z2020-03-26T16:32:39Z2017Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 10139978151060723116057422https://hdl.handle.net/20.500.12585/895210.1117/12.2254518Universidad Tecnológica de BolívarRepositorio UTB571901659395719068845957210822856This paper describes a region growing segmentation algorithm for medical ultrasound images. The algorithm starts with anisotropic diffusion filtering to reduce speckle noise without blurring the edges. Then, region growing is performed starting from a seed point, using a merging criterion that compares intensity gradients to the noise level inside the region. Finally, the boundaries are smoothed using morphological closing. The algorithm was evaluated with two simulated images and eleven phantom images and converged in 10 of them with accurate region delimitation. Preliminary results show that the proposed method can be used for ultrasound image segmentation and does not require previous knowledge of the anatomy of the structures. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.Alpinion Medical Systems;The Society of Photo-Optical Instrumentation Engineers (SPIE)Recurso electrónicoapplication/pdfengSPIEhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/restrictedAccessAtribución-NoComercial 4.0 Internacionalhttp://purl.org/coar/access_right/c_16echttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85020784325&doi=10.1117%2f12.2254518&partnerID=40&md5=7e28cc8593ae2e4f109d27c383ae818dScopus2-s2.0-85020784325Medical Imaging 2017: Ultrasonic Imaging and TomographyRegion growing segmentation of ultrasound images using gradients and local statisticsinfo:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionConferenciahttp://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/resource_type/c_c94fAnisotropic diffusion filteringMedical ultrasoundRegion growing segmentationAnisotropyImaging systemsMedical imagingOptical anisotropyTomographyUltrasonic imagingAnisotropic diffusion filteringIntensity gradientsMedical ultrasoundMedical ultrasound imagesMorphological closingRegion growingSegmentation algorithmsUltrasound image segmentationImage segmentation15 February 2017 through 16 February 2017Kass, M., Witkin, A., Terzopoulos, D., Snakes: Active contour models (1988) International Journal of Computer Vision, 1 (4), pp. 321-331Caselles, V., Kimmel, R., Sapiro, G., Geodesic active contours (1997) International Journal of Computer Vision, 22 (1), pp. 61-79Cootes, T.F., Taylor, C.J., Cooper, D.H., Graham, J., Active shape models-their training and application (1995) Computer Vision and Image Understanding, 61 (1), pp. 38-59Zhan, Y., Shen, D., Deformable segmentation of 3-d ultrasound prostate images using statistical texture matching method (2006) Medical Imaging, IEEE Transactions on, 25 (3), pp. 256-272Wang, W., Qin, J., Chui, Y.-P., Heng, P.-A., A multiresolution framework for ultrasound image segmentation by combinative active contours (2013) Engineering in Medicine and Biology Society (EMBC), 2013 35th Annual International Conference of the IEEE, pp. 1144-1147. , IEEEWang, W., Zhu, L., Qin, J., Chui, Y.-P., Li, B.N., Heng, P.-A., Multiscale geodesic active contours for ultrasound image segmentation using speckle reducing anisotropic diffusion (2014) Optics and Lasers in Engineering, 54, pp. 105-116Gupta, D., Anand, R., Tyagi, B., A hybrid segmentation method based on Gaussian kernel fuzzy clustering and region based active contour model for ultrasound medical images (2015) Biomedical Signal Processing and Control, 16, pp. 98-112Yu, Y., Cheng, J., Li, J., Chen, W., Chiu, B., Automatic prostate segmentation from transrectal ultrasound images (2014) Biomedical Circuits and Systems Conference (BioCAS), 2014 IEEE, pp. 117-120. , IEEEThakur, A., Anand, R.S., A local statistics based region growing segmentation method for ultrasound medical images (2004) International Journal of Signal Processing, 1 (2), pp. 141-146Lee, L.-K., Liew, S.-C., Breast ultrasound automated roi segmentation with region growing (2015) Software Engineering and Computer Systems (ICSECS), 2015 4th International Conference on, pp. 177-182. , IEEEJensen, J.A., Field: A program for simulating ultrasound systems (1996) Medical and Biological Engineering and Computing, 34, pp. 351-352Jensen, J.A., Svendsen, N.B., Calculation of pressure fields from arbitrarily shaped, apodized, and excited ultrasound transducers (1992) IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 39 (2), pp. 262-267Giraldo-Guzman, J., Porto-Solano, O., Cadena-Bonfanti, A., Contreras-Ortiz, S.H., Speckle reduction in echocardiography by temporal compounding and anisotropic diffusion filtering (2015) Tenth International Symposium on Medical Information Processing and Analysis, 92871F-92871F, International Society for Optics and Photonicshttp://purl.org/coar/resource_type/c_c94fTHUMBNAILMiniProdInv.pngMiniProdInv.pngimage/png23941https://repositorio.utb.edu.co/bitstream/20.500.12585/8952/1/MiniProdInv.png0cb0f101a8d16897fb46fc914d3d7043MD5120.500.12585/8952oai:repositorio.utb.edu.co:20.500.12585/89522023-05-25 15:53:58.528Repositorio Institucional UTBrepositorioutb@utb.edu.co |