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AKILLI SÄ°STEMLER VE UYGULAMALARI DERGÄ°SÄ°
JOURNAL OF INTELLIGENT SYSTEMS WITH APPLICATIONS
J. Intell. Syst. Appl.
E-ISSN: 2667-6893
Creative Commons License This work is licensed under a Creative Commons Attribution 4.0 International License.

Determination of Tumor Boundaries in FLAIR Sequence MR Image with Different Image Segmentation Algorithms

FLAIR Sekans MR Görüntüsünde Tümör Sınırlarının Farklı Görüntü Segmentasyon Algoritmaları ile Belirlenmesi

How to cite: Öziç MÃ, GüneÅŸ C, Avcı A. Determination of tumor boundaries in flair sequence mr image with different image segmentation algorithms. Akıllı Sistemler ve Uygulamaları Dergisi (Journal of Intelligent Systems with Applications) 2020; 3(1): 39-42.

Full Text: PDF, in Turkish.

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Title: Determination of Tumor Boundaries in FLAIR Sequence MR Image with Different Image Segmentation Algorithms

Abstract: Tumors are undesired tissue disorders that occur in many different parts of the body. These disorders can be either benign or malignant depending on their type. Brain tumors are non-brain structures that are frequently encountered in neurology. These structures negatively affect daily life by disrupting the functional centers of the person with respect to their region in the brain. Determining certain boundaries of tumor areas in radiology is an important parameter for treatment and diagnosis. In this study, segmentation of the tumor region on FLAIR sequence MR image taken from the BRATS database has been tried with seven different image processing algorithms. Segmentation performances of algorithms have been determined by using dice and jaccard indexes.

Keywords: FLAIR; image processing; MR; segmentation; tumor


Başlık: FLAIR Sekans MR Görüntüsünde Tümör Sınırlarının Farklı Görüntü Segmentasyon Algoritmaları ile Belirlenmesi

Özet: Tümörler, vücudun birçok farklı bölgesinde meydana gelen istenmeyen doku bozukluklarıdır. Bu bozukluklar, tiplerine göre iyi huylu veya kötü huylu olabilir. Beyin tümörleri nörolojide sıklıkla karşılaşılan beyin dışı yapılardır. Bu yapılar beyinde bulundukları bölgeye göre kişinin fonksiyonel merkezlerini bozarak günlük yaşamı olumsuz bir şekilde etkilemektedir. Radyolojide tümörlü bölgelerin net sınırlarının belirlenmesi tedavi ve tanı için önemli bir parametredir. Bu çalışmada, BRATS veri tabanından alınan FLAIR sekans MR görüntüsünde tümörlü bölgenin segmentasyonu yedi farklı görüntü işleme algoritması ile denenmiştir. Dice ve jaccard indeksleri kullanılarak algoritmaların segmentasyon performansları belirlenmiştir.

Anahtar kelimeler: FLAIR; görüntü işleme; MR; segmentasyon; tümör


Bibliography:
  • Zulch KJ. Brain tumors: Their biology and pathology. Springer-Verlag, New York, USA, 2013.
  • Fink JR, Muzi M, Peck M, Krohn KA. Multimodality brain tumor imaging: MR imaging, PET, and PET/MR imaging. Journal of Nuclear Medicine 2015; 56(10): 1554-1561.
  • Ganslandt O, Behari S, Gralla J, Fahlbusch R, Nimsky C. Neuronavigation: Concept, techniques and applications. Neurology India 2002; 50(3): 244-255.
  • Patel J, Doshi K. A study of segmentation methods for detection of tumor in brain MRI. Advance in Electronic and Electric Engineering 2014; 4(3): 279-284.
  • Bakas S, Reyes M, Jakab A, Bauer S, Rempfler M, Crimi A, Shinohara R, Berger C, Ha S, Rozycki M, Prastawa M, Alberts E, Lipkova J, Freymann J, Kirby J, Bilello M, Fathallah-Shaykh H, Wiest R, Kirschke J, Menze B. Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge. University of Cambridge, Repository 2018. Retrieved from https://www.repository.cam.ac.uk/handle/1810/291597
  • Vishnuvarthanan G, Rajasekaran MP, Vishnuvarthanan NA, Prasath TA, Kannan M. Tumor detection in T1, T2, FLAIR and MPR brain images using a combination of optimization and fuzzy clustering improved by seed-based region growing algorithm. International Journal of Imaging Systems and Technology 2017; 27(1): 33-45.
  • Liu J, Li M, Wang J, Wu F, Liu T, Pan Y. A survey of MRI-based brain tumor segmentation methods. Tsinghua Science and Technology 2014; 19(6): 578-595.
  • Kaur A. An automatic brain tumor extraction system using different segmentation methods. In 2016 IEEE Second International Conference on Computational Intelligence & Communication Technology (CICT), February 12-13, 2016, Ghaziabad, India, pp. 187-191.
  • Preim B, Botha CP, Visual Computing for Medicine: Theory, Algorithms, and Applications. Newnes, 2013.
  • Veksler O. Star shape prior for graph-cut image segmentation. In European Conference on Computer Vision, 2008, Springer, pp. 454-467.
  • Chaudhuri D, A. Agrawal A. Split-and-merge procedure for image segmentation using bimodality detection approach. Defence Science Journal 2010; 60(3): 290-301.
  • Dey N. Classification Techniques for Medical Image Analysis and Computer Aided Diagnosis. Academic Press, 2019.
  • Chang HH, Zhuang AH, Valentino DJ, Chu WC. Performance measure characterization for evaluating neuroimage segmentation algorithms. Neuroimage 2009; 47(1): 122-135.