Research Article
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THE EFFECTS OF NOISE FILTERS ON SEGMENTATION BASED SEEDED REGION GROWING

Year 2019, Volume: 7 Issue: 4, 725 - 735, 19.12.2019
https://doi.org/10.21923/jesd.420101

Abstract

Image segmentation is a
process of grouping pixels to make parts of objects into distinct image areas
using their texture, edge, color properties. The segmentation process plays an
important role in the analysis of images and in image processing. One of the
techniques developed for segmentation is SRG (Seeded Region Growing). The noise
generated during the acquisition of images affects the segmentation success
negatively. Filters used to eliminate noise reduce it, but the effect of
filtering on the segmentation success is not fully known. In this study, the
effects of noise and filters on the SRG algorithm are investigated. For this
purpose, various noises were added to Weizmann database images at different
levels. Later, filters were applied to noisy images. Finally, F-Score values
were obtained from the images segmented by the SRG algorithm and compared with
the values of the original images.

References

  • Adams, R., Bischof, L., 1994. Seeded Region Growing. IEEE Transactions on pattern analysis and machine intelligence, 16(6), 641-647.
  • Al-Faris, A.Q., Ngah, U.K., Isa, N.A.M., Shuaib, I.L., 2014. Breast MRI Tumour Segmentation Using Modified Automatic Seeded Region Growing Based on Particle Swarm Optimization Image Clustering. Soft Computing in Industrial Applications, vol 223, In: Snášel V., Krömer P., Köppen M., Schaefer G. (eds), Advances in Intelligent Systems and Computing, Springer, Cham.
  • Alpert, S., Galun, M., Brandt, A., Basri, R., 2012. Image Segmentation by Probabilistic Bottom-Up Aggregation and Cue Integration. IEEE transactions on pattern analysis and machine intelligence, 34(2), 315-327.
  • Dreizin, D., Bodanapally, U.K., Neerchal, N., Tirada, N., Patlas, M., Herskovits, E., 2016. Volumetric Analysis of Pelvic Hematomas After Blunt Trauma Using Semi-Automated Seeded Region Growing Segmentation: A Method Validation Study. Abdominal Radiology, 41(11), 2203-2208.
  • Fan, J., Yau, D.K., Elmagarmid, A.K., Aref, W.G., 2001. Automatic Image Segmentation by Integrating Color-Edge Extraction and Seeded Region Growing. IEEE transactions on image processing, 10(10), 1454-1466.
  • Gómez, O., González, J.A. Morales, E.F., November, 2007. Image Segmentation Using Automatic Seeded Region Growing and Instance-Based Learning. In Iberoamerican Congress on Pattern Recognition, Valparaíso, Chile, 192-201.
  • Gonzalez, R.C., Woods, R.E., Eddins, S.L., 2009. Digital Image Processing Using MATLAB. Gatesmark Publishing.
  • İncetaş, M.O., Kılıçaslan, M., Tanyeri, U., Yakışır Girgin, B., Aydemir, Z., Kasım, 2017. Gürültünün Tohumlu Alan Genişletme Tabanlı Bölütleme Sonucuna Etkisinin Nicemsel Olarak Belirlenmesi. Uluslararası Multidisipliner Çalışmalar ve Yenilikçi Teknolojiler Sempozyumu ISMSIT, Tokat, Türkiye.
  • Kostopoulos, S.A., Vassiou, K.G., Lavdas, E.N., Cavouras, D.A., Kalatzis, I.K., Asvestas, P.A., Arvanitis, D.L., Fezoulidis, I.V., Glotsos, D.T., 2017. Computer-Based Automated Estimation of Breast Vascularity and Correlation with Breast Cancer in DCE-MRI Images. Magnetic resonance imaging, 35, 39-45.
  • Pan, J., Wang, M., 2016. Improved Seeded Region Growing for Detection of Water Bodies in Aerial Images. Geo-spatial Information Science, 19(1), 1-8.
  • Pohle, R., Toennies, K.D., July, 2001. Segmentation of Medical Images Using Adaptive Region Growing. In Medical Imaging 2001: Image Processing, San Diego, CA, United States, International Society for Optics and Photonics, 4322, 1337-1347.
  • Samantaray, M., Panigrahi, M., Patra, K.C., Panda, A.S., Mahakud, R., January, 2016. An Adaptive Filtering Technique for Brain Tumor Analysis and Detection. In Intelligent Systems and Control (ISCO), 10th International Conference on IEEE, Tamilnadu, India.
  • Savkare, S.S., Narote, A.S., Narote, S.P., September, 2016. Automatic Blood Cell Segmentation Using K-Mean Clustering from Microscopic Thin Blood Images. In Proceedings of the Third International Symposium on Computer Vision and the Internet ACM, 8-11, Jaipur, India.
  • Wu, J., Poehlman, S., Noseworthy, M.D., Kamath, M.V., May, 2008. Texture Feature Based Automated Seeded Region Growing in Abdominal MRI Segmentation. In BioMedical Engineering and Informatics, Sanya, China, 2, 263-267.
  • Yeom, J., Jung, M., Kim, Y., 2017. Detecting Damaged Building Parts in Earthquake-Damaged Areas Using Differential Seeded Region Growing. International journal of remote sensing, 38(4), 985-1005.

GÜRÜLTÜ FİLTRELERİNİN TOHUMLU ALAN GENİŞLETME TABANLI BÖLÜTLEMEYE ETKİLERİ

Year 2019, Volume: 7 Issue: 4, 725 - 735, 19.12.2019
https://doi.org/10.21923/jesd.420101

Abstract

Görüntü
bölütleme, doku, kenar ve renk özelliklerini kullanarak nesnelerin parçalarını
farklı görüntü alanlarına dönüştürmek için pikselleri gruplama işlemidir.
Bölütleme süreci, görüntülerin analizinde ve görüntü işlemede önemli bir rol
oynar. Bölütleme için geliştirilen tekniklerden biri de SRG'dir (Tohumlu Alan
Genişletme). Görüntülerin elde edilmesi sırasında oluşan gürültü, bölütleme
başarısını olumsuz yönde etkiler. Gürültüyü ortadan kaldırmak için kullanılan
filtreler gürültüyü azaltmaktadır, ancak filtreleme işleminin bölütleme
başarısı üzerindeki etkisi tam olarak bilinmemektedir. Bu çalışmada, gürültü ve
filtrelerin SRG algoritması üzerindeki etkileri araştırılmıştır. Bu amaçla
Weizmann veri tabanına farklı seviyelerde çeşitli gürültüler eklenmiştir. Daha
sonra gürültülü görüntülere filtreler uygulanmıştır. Son olarak, SRG
algoritması tarafından segmentlere ayrılmış görüntülerden F-Skor değerleri elde
edilmiş ve orijinal görüntülerin değerleri ile karşılaştırılmıştır.

References

  • Adams, R., Bischof, L., 1994. Seeded Region Growing. IEEE Transactions on pattern analysis and machine intelligence, 16(6), 641-647.
  • Al-Faris, A.Q., Ngah, U.K., Isa, N.A.M., Shuaib, I.L., 2014. Breast MRI Tumour Segmentation Using Modified Automatic Seeded Region Growing Based on Particle Swarm Optimization Image Clustering. Soft Computing in Industrial Applications, vol 223, In: Snášel V., Krömer P., Köppen M., Schaefer G. (eds), Advances in Intelligent Systems and Computing, Springer, Cham.
  • Alpert, S., Galun, M., Brandt, A., Basri, R., 2012. Image Segmentation by Probabilistic Bottom-Up Aggregation and Cue Integration. IEEE transactions on pattern analysis and machine intelligence, 34(2), 315-327.
  • Dreizin, D., Bodanapally, U.K., Neerchal, N., Tirada, N., Patlas, M., Herskovits, E., 2016. Volumetric Analysis of Pelvic Hematomas After Blunt Trauma Using Semi-Automated Seeded Region Growing Segmentation: A Method Validation Study. Abdominal Radiology, 41(11), 2203-2208.
  • Fan, J., Yau, D.K., Elmagarmid, A.K., Aref, W.G., 2001. Automatic Image Segmentation by Integrating Color-Edge Extraction and Seeded Region Growing. IEEE transactions on image processing, 10(10), 1454-1466.
  • Gómez, O., González, J.A. Morales, E.F., November, 2007. Image Segmentation Using Automatic Seeded Region Growing and Instance-Based Learning. In Iberoamerican Congress on Pattern Recognition, Valparaíso, Chile, 192-201.
  • Gonzalez, R.C., Woods, R.E., Eddins, S.L., 2009. Digital Image Processing Using MATLAB. Gatesmark Publishing.
  • İncetaş, M.O., Kılıçaslan, M., Tanyeri, U., Yakışır Girgin, B., Aydemir, Z., Kasım, 2017. Gürültünün Tohumlu Alan Genişletme Tabanlı Bölütleme Sonucuna Etkisinin Nicemsel Olarak Belirlenmesi. Uluslararası Multidisipliner Çalışmalar ve Yenilikçi Teknolojiler Sempozyumu ISMSIT, Tokat, Türkiye.
  • Kostopoulos, S.A., Vassiou, K.G., Lavdas, E.N., Cavouras, D.A., Kalatzis, I.K., Asvestas, P.A., Arvanitis, D.L., Fezoulidis, I.V., Glotsos, D.T., 2017. Computer-Based Automated Estimation of Breast Vascularity and Correlation with Breast Cancer in DCE-MRI Images. Magnetic resonance imaging, 35, 39-45.
  • Pan, J., Wang, M., 2016. Improved Seeded Region Growing for Detection of Water Bodies in Aerial Images. Geo-spatial Information Science, 19(1), 1-8.
  • Pohle, R., Toennies, K.D., July, 2001. Segmentation of Medical Images Using Adaptive Region Growing. In Medical Imaging 2001: Image Processing, San Diego, CA, United States, International Society for Optics and Photonics, 4322, 1337-1347.
  • Samantaray, M., Panigrahi, M., Patra, K.C., Panda, A.S., Mahakud, R., January, 2016. An Adaptive Filtering Technique for Brain Tumor Analysis and Detection. In Intelligent Systems and Control (ISCO), 10th International Conference on IEEE, Tamilnadu, India.
  • Savkare, S.S., Narote, A.S., Narote, S.P., September, 2016. Automatic Blood Cell Segmentation Using K-Mean Clustering from Microscopic Thin Blood Images. In Proceedings of the Third International Symposium on Computer Vision and the Internet ACM, 8-11, Jaipur, India.
  • Wu, J., Poehlman, S., Noseworthy, M.D., Kamath, M.V., May, 2008. Texture Feature Based Automated Seeded Region Growing in Abdominal MRI Segmentation. In BioMedical Engineering and Informatics, Sanya, China, 2, 263-267.
  • Yeom, J., Jung, M., Kim, Y., 2017. Detecting Damaged Building Parts in Earthquake-Damaged Areas Using Differential Seeded Region Growing. International journal of remote sensing, 38(4), 985-1005.
There are 15 citations in total.

Details

Primary Language English
Subjects Computer Software
Journal Section Araştırma Articlessi \ Research Articles
Authors

Mürsel Ozan İncetaş 0000-0002-1016-1655

Ufuk Tanyeri 0000-0002-7039-9577

Publication Date December 19, 2019
Submission Date May 1, 2018
Acceptance Date May 17, 2019
Published in Issue Year 2019 Volume: 7 Issue: 4

Cite

APA İncetaş, M. O., & Tanyeri, U. (2019). THE EFFECTS OF NOISE FILTERS ON SEGMENTATION BASED SEEDED REGION GROWING. Mühendislik Bilimleri Ve Tasarım Dergisi, 7(4), 725-735. https://doi.org/10.21923/jesd.420101