Brain Tumor Segmentation through Region-based, Supervised and Unsupervised Learning Methods: A Literature Survey

Brain Tumor Segmentation through Image Processing Methods: A Literature Survey

  • Muhammad Zawish Department of Computer Science & Technology,  Mehran University of Engineering & Technology
  • Asad Ali Siyal Department of Biomedical Engineering,Mehran University of Engineering & Technology (MUET),Jamshoro, Sindh, Pakistan
  • Shahzad Hyder Shahani Department of Computer Science & Technology,  Mehran University of Engineering & Technology
  • Aisha Zahid Junejo Department of Computer Science & Technology,  Mehran University of Engineering & Technology
  • Aiman Khalil Department of Computer Science & Technology,  Mehran University of Engineering & Technology
Keywords: Image Analysis, Image Segmentation, Brain Tumor Detection Region Based, Supervised Learning, Unsupervised Learning, Clustering, Watershed Segmentation, Convolutional Neural Networks, SVM, K-Means Clustering, MRI, CT scan

Abstract

Image segmentation is one of the most trending fields in the domain of digital image processing. For years, researchers have shown a remarkable progress in the field of Image Segmentation, precisely, for brain tumor extraction from various medical imaging modalities including X-Ray, Computed Tomography and most importantly, Magnetic Resonance Images (MRI). In these medical imaging modalities, accurate and reliable brain tumor segmentation is extremely imperative to perform safe diagnose, healthy treatment planning and consistent treatment outcome evaluation in order to understand and cure the complexities of chronic diseases such as Cancer. This paper presents various image processing techniques that are currently being used for brain tumor extraction from medical images. Though some great work has been done in this domain but none of the techniques has been widely accepted to be brought into practice in real time clinical analysis. The paper concludes with proposing some solutions that would aid in refining the results of the techniques which will lead to clinical acceptance of these computer aided methods.

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Published
2019-07-03