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A cooperative framework for automated segmentation of tumors in brain MRI images

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Abstract

Brain tumor segmentation from 2D Magnetic Resonance Images (MRI) is an important task for several applications in the field of medical analysis. Commonly, this task is performed manually by medical professionals, but it is not always obvious due to similarities between tumors and normal tissue and variations in tumor appearance. Therefore, the automation of medical image segmentation remains a real challenge that has attracted the attention of several researchers in recent years. Instead of choosing between region and contour approaches, in this article, we propose a region-edge cooperative method for brain tumor segmentation from MRI images. The region approach used is support vector machines (SVMs), one of the popular and highly motivated classification methods, the method distinguishes between normal and abnormal pixels based on some features: intensity and texture. To control and guide the segmentation region, we take advantage of the Ron Kimmel geodesic Active Contour Model (ACM) which produces a good delimitation of the boundaries of the object. The two methods have been cooperated sequentially in order to obtain a flexible and effective system for brain tumor segmentation. Experimental studies are performed on synthetic and real 2D MRI images of various modalities from the radiology unit of the university hospital center in Bab El Oued Algeria. The used MRI images represent various tumor shapes, locations, sizes, and intensities. The proposed cooperative framework outperformed SVM-based segmentation and ACM-based segmentation when executed independently.

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Data Availability

All the data and materials could be found at SISM Division of the Research Center on Scientific and Technical Information (CERIST).

Abbreviations

MRI:

Magnetic Resonance Image

WM:

White Matter

GM:

Gray Matter

CSF:

CerebroSpinal Fluid

SVM:

Support Vector Machine

ACM:

Active Contour Model

References

  1. Allioui H, Sadgal M, El Faziki A (2018) Alzheimer detection based on multi-agent systems: an intelligent image processing environment. In: International conference on advanced intelligent systems for sustainable development, pp 314—326. https://doi.org/10.1007/978-3-030-11884-6_28

  2. Allioui H, Sadgal M, El Fazziki A (2019) A robust multi-agent negotiation for advanced image segmentation: design and implementation. Intel Artif 22(64):102—122. https://doi.org/10.4114/intartif.vol22iss64pp102-122

    Article  Google Scholar 

  3. Allioui H, Sadgal M, El Fazziki A (2020) An improved image segmentation system: a cooperative multi-agent strategy for 2D/3D medical images. J Commun Softw Sys 16(2):143—154. https://doi.org/10.24138/jcomss.v16i2.830

    Article  Google Scholar 

  4. Amiri S, Rekik I, Mahdjoub MA (2016) Deep random forest-based learning transfer to SVM for brain tumor segmentation. 2nd Int Conf Adv Technol Signal Image Process, pp 297–302. https://doi.org/10.1109/ATSIP.2016.7523095

  5. Bakkouri I, Afdel K (2020) Computer-aided diagnosis (CAD) system based on multi-layer feature fusion network for skin lesion recognition in dermoscopy images. Multimed Tools Appl 79:20483–20518. https://doi.org/10.1007/s11042-019-07988-1

    Article  Google Scholar 

  6. Bakkouri I, Afdel K (2022) MLCA2F: Multi-level context attentional feature fusion for COVID-19 lesion segmentation from CT scans. Signal Image Video Process. https://doi.org/10.1007/s11760-022-02325-w

  7. Benhamza K, Seridi H (2020) Intelligent cooperative method for medical image segmentation. In: The 4th international conference on electrical engineering and control applications, pp 1221–1231. https://doi.org/10.1007/978-981-15-6403-1_86

  8. Bensalem S (2014) Segmentation Automatique de Tumeur cérébrale par Contour Actif Contraint par des relations spatiales. Theses, Saad Dahleb University in Blida

  9. Boudraa O, Benatchba K (2015) Region-edge cooperation for image segmentation using game theory. In: IFIP international conference on computer science and its applications. Springer, Cham, pp 515–526. https://doi.org/10.1007/978-3-319-19578-0_42

  10. Caselles V, Catte F, Coll T, Dibos F (1993) A geometric model for active contours. Numer Math 66:1–31. https://doi.org/10.1007/BF01385685

    Article  MathSciNet  MATH  Google Scholar 

  11. Chan TF, Vese LA (2001) Active contour without edges. IEEE Trans Image Process 10(2):266–277. https://doi.org/10.1109/83.902291

    Article  MATH  Google Scholar 

  12. Drevelegas A, Papanikolaou N (2011) Imaging Modalities in Brain Tumors. In: Drevelegas A (ed) Imaging of brain tumors with histological correlations. Springer, Berlin. https://doi.org/10.1007/978-3-540-87650-2_2

  13. Gupta B, Tiwari S (2014) Brain tumor detection using curvelet transform and support vector machine. Int J Comput Sci Mobile Comput 3(4):1259–1264

    Google Scholar 

  14. Hamad YA, Simonov KV, Naeem M (2019) Detection of brain tumor in MRI images, using a combination of fuzzy C-Means and thresholding. Int J Adv Pervasive Ubiquitous Comput 11(1):45–60. https://doi.org/10.4018/IJAPUC.2019010104

    Article  Google Scholar 

  15. Haroun R, Boumghar F, Hassas S, Hamami L (2004) A massive multi-agent system for brain MRI segmentation. Int Work Massively Multi-Agent Syst, pp 174—186. https://doi.org/10.1007/11512073_13

  16. Islam M, Kashem M (2021) Parametric active contour model-based tumor area segmentation from brain mri images using minimum initial points. Iran J Comput Sci 4(2):125–132. https://doi.org/10.1007/s42044-020-00078-8

    Article  Google Scholar 

  17. Jayadevappa D, Kumar S, Murthy D (2009) A hybrid segmentation model based on watershed and gradient vector flow for the detection of brain tumor. Int J Signal Process Image Process Pattern Recognit 2:29—42

    Google Scholar 

  18. Kimmel R (2003) Fast edge integration. In: Geometric level set methods in imaging, vision, and graphics. Springer, New York. https://doi.org/10.1007/0-387-21810-6_4

  19. Lakshmi Narayanan K, Niranjana R, Francy Irudaya Rani E, Subbulakshmi N, Santhana Krishnan R (2021) Powerful and novel tumour detection in brain MRI images employing hybrid computational techniques. Recent Trends Intensive Comput 39:777–782. https://doi.org/10.3233/APC210279

    Article  Google Scholar 

  20. Luo Y, Liu L, Huang Q, Li X (2017) A novel segmentation approach combining region-and edge-based information for ultrasound images. BioMed Res Int. https://doi.org/10.1155/2017/9157341

  21. Malladi R, Sethian JA, Vemuri BC (1995) Shape modeling with front propagation: a level set approach. IEEE Trans PAMI 17:158–175. https://doi.org/10.1109/34.368173

    Article  Google Scholar 

  22. Menze B, et al. (2015) The multimodal brain tumor image segmentation benchmark (brats). IEEE Trans Med Imaging 34(10):1993–2024. https://doi.org/10.1109/TMI.2014.2377694

    Article  Google Scholar 

  23. Nachour A, Ouzizi L, Aoura Y (2016) Multi-agent segmentation using region growing and contour detection: syntetic evaluation in MR images with 3D CAD reconstruction. Int J Comput Inf Syst Ind Manag Appl 8:115–124

    Google Scholar 

  24. Osher SJ, Sethian JA (1988) Fronts propagation with curvature dependent speed: algorithms based on Hamilton-Jacobi formulations. J Comput Phys 79:12–49. https://doi.org/10.1016/0021-9991(88)90002-2

    Article  MathSciNet  MATH  Google Scholar 

  25. Parveen SA (2015) Detection of brain tumor in MRI images, using combination of fuzzy C-Means and SVM. Int Conf Signal Process Integr Netw, pp 98–102. https://doi.org/10.1109/SPIN.2015.7095308

  26. Rajesh Babu K, Naganjaneyulu PV, Satya Prasad K (2021) Comparative analysis of active contour models for brain tumor segmentation from T1w MRI images. 2021 Int Conf Comput Commun Inform (ICCCI):1–6. https://doi.org/10.1109/ICCCI50826.2021.9402433

  27. Shariatpanahi HF, Batmanghelich N, Kermani AR, Ahmadabadi MN, Soltanian-Zadeh H (2006) Distributed behavior-based multi-agent system for automatic segmentation of brain MR images. In: The 2006 IEEE international joint conference on neural network proceedings, pp 4535–4542. https://doi.org/10.1109/IJCNN.2006.247079

  28. Sulaiman SN, Mat Isa NA (2010) Adaptive fuzzy-K-means clustering algorithm for image segmentation. IEEE Trans Consum Electron 56(4):2661—2668. https://doi.org/10.1109/TCE.2010.5681154

    Article  Google Scholar 

  29. Vapnik V (1995) The Nature of Statistical Learning Theory. Springer, New York

    Book  MATH  Google Scholar 

  30. Zararsiz G, Elmali F, Ozturk A (2012) Bagging support vector machines for leukemia classification. Int J Comput Sci Issues 9(6):355–358

    Google Scholar 

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Acknowledgements

Authors would like to express their gratitude to Dr. Mohamed Amine Guerroudji from CDTA (Center for Development of Advanced Technologies) for his thorough remarks and guidance that helped in revising the paper.

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  1. Research Center on Scientific and Technical Information (CERIST), Algiers, Algeria

    Zineb Hadjadj

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  1. Zineb Hadjadj
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Hadjadj, Z. A cooperative framework for automated segmentation of tumors in brain MRI images. Multimed Tools Appl 82, 41381–41404 (2023). https://doi.org/10.1007/s11042-023-14736-z

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