Indian Journal of Science and Technology

Open Access Open Access  Restricted Access Subscription Access

Fuzzy Qualitative Reasoning Model for Astrocytoma Brain Tumor Grade Diagnosis


Affiliations
1 Department of Computer Science and Engineering, SSN College of Engineering, Chennai – 603110, Tamil Nadu, India
2 Department of Electronics and Communication Engineering, RMD Engineering College, Chennai – 601206, Tamil Nadu, India
 

Background: Magnetic Resonance Imaging (MRI) is the most prominently used image acquisition method for brain tumor diagnosis, treatment and research. Objective: In this paper, a fuzzy qualitative reasoning model for diagnosing the grade of Astrocytoma brain tumor using various subtypes of MR images (T1, T1c+, T2, Flair) is explained with its implementation details. Methods: The fuzzy model is implemented in 5 stages namely preprocessing, segmentation, feature extraction, feature selection and building a Fuzzy Inference System (FIS) for diagnosis. In preprocessing, anisotropic filtering is used to remove noise and artifacts whereas the edge information and smoothness are retained. Then the tumor region is segmented by applying active contour method. From the segmented tumor region, textural and shape features are extracted and stored along with the clinical parameters like age, gender and mass effect of the patient for feature selection. The features are analyzed in different dimensions like image, patient, patient with subtype, to determine the sensitive feature subset and its range that discriminates the grade of the tumor. Based on this outcome a Mamdani based fuzzy qualitative reasoning model is built with optimal rule set for tumor grade diagnosis. Findings: The constructed fuzzy model is validated using real data set of MR images and clinical report of patients. The grade of tumor identified is same as that specified in the patient's report and hence the model provides better accuracy. Novelty: The novelty of this research work are: subtypes of MR images with analysis in different dimensions, identification of optimal rule set (minimum number of rules without ambiguity), recognition of irregular shape tumor, suitable model for any knowledge based diagnosis.
User

  • Brant WE, Helms CA. Fundamentals of Diagnostic Radiology. Fourth Edition, Lippincott Williams & Wilkins; 2012.

  • Bauer S, Wiest R, Nolte L, Reyes M. A survey of MRI based medical image analysis for brain tumor studies. Physics in Medicine and Biology. 2013; 58(13):97–129. https://doi.org/10.1088/0031-9155/58/13/R97. PMid:23743802.

  • Kuperman V. Magnetic resonance imaging: Physical principles and applications. Academic Press; 2000.

  • Kiranmayee BV, Rajinikanth TV, Nagini S. Enhancement of SVM based MRI brain image classification using preprocessing techniques. Indian Journal of Science and Technology. 2016; 9(29):1–7. https://doi.org/10.17485/ijst/2016/v9i29/91042.

  • American brain tumor association [Internet]. [cited 2017 Aug 15]. Available from: http://www.abta.org.

  • EEl-Dahshan EA, Mohsen HM, Revett K, Salem MA. Computer-aided diagnosis of human brain tumor through MRI: A survey and a new algorithm. Expert Systems with Applications. 2014; 41(11):5526–45. https://doi.org/10.1016/j.eswa.2014.01.021.

  • Atif J, Hudelot C, Fouquier G, Bloch I, Angelini E. From generic knowledge to specific reasoning for medical image interpretation using graph based representations. Proceedings of the 20th International Joint Conference on Artificial Intelligence; 2007. p. 224–9.

  • Divya P, Geetha R, Kavitha S. Semi-automatic detection of fracture in wrist bones using graph-based grammar approach. IEEE International Conference on Computing Communications and Networking Technologies (ICCCNT'12); 2012. p. 1–7.

  • Chen NY, Geng DY, Yang J, Ye CZ, Zhou Y. Fuzzy rules to predict degree of malignancy in brain glioma. Medical and Biological Engineering and Computing. 2002; 40(2):145– 52. https://doi.org/10.1007/BF02348118. PMid:12043794.

  • Zarandi FMH, Izadi M, Zarinbal M. Systematic image processing for diagnosing brain tumors: A type-II fuzzy expert system approach, Applied Soft Computing. 2011; 11(1):285–94. https://doi.org/10.1016/j.asoc.2009.11.019.

  • Sikchi SS, Sikchi S, Ali MS. Generic medical fuzzy expert system for diagnosis of cardiac diseases. International Journal of Computer Applications. 2013; 66(13):35–44.

  • Neshat M, Adeli A. A fuzzy expert system for Heart disease diagnosis. Proceedings of the International Multi Conference of Engineers and Computer Scientists (IMECS); 2010. p. 1–6.

  • Papageorgiou EI, Spyridonos PP, Glotsos D, Stylios CD, Ravazoula P, Groumpos PP, Nikiforidis GN. Groumpos PP. Brain tumor characterization using the soft computing technique of fuzzy cognitive maps. Applied Soft Computing. 2008; 8(1):820–8. https://doi.org/10.1016/j.asoc.2007.06.006.

  • Samuel OW, Omisore MO, Ojokoh BA. A web based decision support system driven by fuzzy logic for the diagnosis of typhoid fever. Expert Systems with Applications. 2013; 40(10):4164–71. https://doi.org/10.1016/j.eswa.2013.01.030.

  • Louis DN, Ohgaki H, Wiestler O, Cavenee WK, Burger PC, Jouvet A, Scheithauer BW, Kleihues P. The 2007 WHO classification of tumors of the central nervous system. Acta Neuropathologica. 2007; 114(2):97–109. https://doi.org/10.1007/s00401-007-0243-4. PMid:17618441. PMCid:PMC1929165.

  • Gupta S, Walia P, Singla C, Dhankar S, Mishra T, Khandelwal A, Bhardwaj M. Segmentation feature extraction and classification of astrocytoma in MR images. Indian Journal of Science and Technology. 2016; 9(36):1–7. https://doi.org/10.17485/ijst/2016/v9i36/102154.

  • Shenbagarajan A, Ramalingam V, Balasubramanian C, Palanivel S. Tumor diagnosis in MRI brain image using ACM segmentation and ANN-LM classification techniques. Indian Journal of Science and Technology. 2016; 9(1):1–12. https://doi.org/10.17485/ijst/2016/v9i1/78766.

  • Malik J, Perona P. Scale-space and edge detection using anisotropic diffusion. IEEE Transactions on Pattern Analysis and Machine Intelligence. 1990; 12(7):629–39. https://doi.org/10.1109/34.56205.

  • Vese LA, Chan TF. Active contours without edges. IEEE Transactions on Image Processing. 2001; 10(2):266–77. https://doi.org/10.1109/83.902291. PMid:18249617.

  • Tran TT, Pham VT, Chiu YJ, Shyu KK. Active contour with selective local or global segmentation for intensity inhomogeneous image. IEEE International Conference on Computer Science and Information Technology; 2010. p. 306–10. PMid:20887267

  • Corso JJ, Zhao L, Wu W. Brain tumor segmentation based on GMM and Active contour method with a model-aware edge map. Proceedings of MICCAI-BRATS; 2012. p. 1–4.

  • Balsiger F. Brain tumor volume calculation. Linkoping University, Sweden; 2012. p. 1–47.

  • Harvard medical school [Internet]. [cited 2017 Aug 28]. Available from: http://www.med.harvard.edu/aanlib/home.html.

  • Medical images [Internet]. [cited 2017 Aug 07]. Available from: http://radiopaedia.org/.

  • Medical images : Bharat Scans, Chennai, 2015


Abstract Views: 189

PDF Views: 0




  • Fuzzy Qualitative Reasoning Model for Astrocytoma Brain Tumor Grade Diagnosis

Abstract Views: 189  |  PDF Views: 0

Authors

S. Kavitha
Department of Computer Science and Engineering, SSN College of Engineering, Chennai – 603110, Tamil Nadu, India
K. K. Thyagharajan
Department of Electronics and Communication Engineering, RMD Engineering College, Chennai – 601206, Tamil Nadu, India

Abstract


Background: Magnetic Resonance Imaging (MRI) is the most prominently used image acquisition method for brain tumor diagnosis, treatment and research. Objective: In this paper, a fuzzy qualitative reasoning model for diagnosing the grade of Astrocytoma brain tumor using various subtypes of MR images (T1, T1c+, T2, Flair) is explained with its implementation details. Methods: The fuzzy model is implemented in 5 stages namely preprocessing, segmentation, feature extraction, feature selection and building a Fuzzy Inference System (FIS) for diagnosis. In preprocessing, anisotropic filtering is used to remove noise and artifacts whereas the edge information and smoothness are retained. Then the tumor region is segmented by applying active contour method. From the segmented tumor region, textural and shape features are extracted and stored along with the clinical parameters like age, gender and mass effect of the patient for feature selection. The features are analyzed in different dimensions like image, patient, patient with subtype, to determine the sensitive feature subset and its range that discriminates the grade of the tumor. Based on this outcome a Mamdani based fuzzy qualitative reasoning model is built with optimal rule set for tumor grade diagnosis. Findings: The constructed fuzzy model is validated using real data set of MR images and clinical report of patients. The grade of tumor identified is same as that specified in the patient's report and hence the model provides better accuracy. Novelty: The novelty of this research work are: subtypes of MR images with analysis in different dimensions, identification of optimal rule set (minimum number of rules without ambiguity), recognition of irregular shape tumor, suitable model for any knowledge based diagnosis.

References





DOI: https://doi.org/10.17485/ijst%2F2018%2Fv11i38%2F107253