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Automatic brain tissue detection in MRI images using seeded region growing segmentation and neural network classification

Jafari, M ; Sharif University of Technology | 2011

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  1. Type of Document: Article
  2. Publisher: 2011
  3. Abstract:
  4. This paper presents a neural network-based method for automatic classification of magnetic resonance images (MRI) of brain under three categories of normal, lesion benign, and malignant. The proposed technique consists of six subsequent stages; namely, preprocessing, seeded region growing segmentation, connected component labeling (CCL), feature extraction, feature Dimension Reduction, and classification. In the preprocessing stage, the enhancement and restoration techniques are used to provide a more appropriate image for the subsequent automated stages. In the second stage, the seeded region growing segmentation is used for partitioning the image into meaningful regions. In the third stage, once all groups have been determined, each pixel is labeled according to the component to which it is assigned to. In the fourth stage, we have obtained the feature related to MRI images using the discrete wavelet transform (DWT). In the fifth stage, the dimension of obtained DWT features are reduced, using the principal component analysis (PCA), to obtain more essential features. In the classification stage, a supervised feed-forward back-propagation neural network technique is used to classify the subjects to normal or abnormal (benign, malignant). We have applied this method on 2D axial slices of 10 different patient data sets and show that the proposed technique gives good results for brain tissue detection and is more robust and effective compared with other recent works
  5. Keywords:
  6. Artificial neural network ; Brain tumor detection ; Classification ; Connected component labeling ; Feature extraction and selection ; Seeded region growing segmentation
  7. Source: Australian Journal of Basic and Applied Sciences ; Volume 5, Issue 8 , 2011 , Pages 1066-1079 ; 19918178 (ISSN)
  8. URL: http://ajbasweb.com/old/ajbas/2011/August-2011/1066-1079.pdf