Total views : 242

Segmentation, Feature Extraction and Classification of Astrocytoma in MR Images


  • Chitkara University, Rajpura - 140401, Punjab, India


The purpose of this research paper is to segment tumorous tissue from brain magnetic resonance images using image processing. For this the astrocytoma, a type of brain tumor is considered in which cells grow abnormally in the brain. Astrocytoma is classified as low grade or high grade using k-nn classifier and then analyzed its performance on the basis of three parameters i.e., accuracy, severity and specificity. The Magnetic Resonance Images (MRI) of astrocytoma has been taken from BRATS database so as to explore different algorithms for segmentation of brain tumour. The enhancement of MRI images is done using Contrast Limited Adaptive Histogram Equalization (CLAHE). After that basic global thresholding method is used for the automatic segmentation of tumorous tissue. In feature extraction step, the shape based features and texture based features are extracted. On the basis of features extracted from the segmented image, K-nn classifier is used to classify the images in two grades i.e., low grade or high grade. The performance of the system is evaluated by parameters accuracy, severity and specificity. The accuracy is coming out to be 93%.


Astrocytoma, CLAHE, Global Thresholding, Grading, GLCM, k-NN Classifier, Tumor.

Full Text:

 |  (PDF views: 315)


  • Louis DN, Ohgaki H, Wiestler OD, Cavenee WK, Burger PC, Jouvet A, Scheithauer BW, Kleihues P. The 2007 WHO classification of tumours of the central nervous system. Acta neuropathologica. 2007 Aug; 114(2):97–109.
  • Natarajan P, Krishnan N, Kenkre NS, Nancy S, Singh BP. Tumor detection using threshold operation in MRI brain images. IEEE International Conference on Computational Intelligence and Computing Research (ICCIC); 2012 Dec. p. 1–4.
  • Ehab FB, Esraa GM, Nadder H. An algorithm for detecting astrocytomas in MRI images. IEEE International Conference on Computer Engineering and Systems (ICCES); 2010 Dec. p. 368–73.
  • Kumar BS, Selvi RA. Feature extraction using image mining techniques to identify brain tumors. 2015 International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS); 2015 Mar. p. 1–6.
  • Patel O, Maravi YP, Sharma S. A comparative study of histogram equalization based image enhancement techniques for brightness preservation and contrast enhancement. arXiv preprint; 2013 Nov.
  • Gupta V, Sagale KS. Implementation of classification system for brain cancer using backpropagation network and MRI. 2012 Nirma University International Conference on Engineering (NUiCONE); 2012 Dec 6. p. 1–4.
  • Vaidyanathan M, Clarke LP, Velthuizen RP, Phuphanich S, Bensaid AM, Hall LO, Bezdek JC, Greenberg H, Trotti A, Silbiger M. Comparison of supervised MRI segmentation methods for tumor volume determination during therapy. Magnetic Resonance Imaging. 1995 Dec; 13(5):719–28.
  • Deekshatulu BL, Chandra P. Classification of heart disease using k-nearest neighbor and genetic algorithm. Procedia Technology. 2013 Dec; 10:85–94.
  • Zhang G, Lu Z, Ji G, Sun P, Yang J, Zhang Y. Automated classification of brain MR images by wavelet-energy and k-nearest neighbors algorithm. 2015 Seventh International Symposium on Parallel Architectures, Algorithms and Programming (PAAP); 2015 Dec. p. 87–91.


  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.