Review on Machine Learning Based Lesion Segmentation Methods from Brain MR Images

GÖÇERİ E., Dura E., GÜNAY M.

15th IEEE International Conference on Machine Learning and Applications (ICMLA), California, United States Of America, 18 - 20 December 2016, pp.582-587, (Full Text) identifier identifier

  • Publication Type: Conference Paper / Full Text
  • Doi Number: 10.1109/icmla.2016.192
  • City: California
  • Country: United States Of America
  • Page Numbers: pp.582-587
  • Keywords: Brain tumor, brain lesion, lacunar infarct, machine learning MRI, neural networks, WHITE-MATTER HYPERINTENSITIES, SMALL VESSEL DISEASE, AUTOMATIC SEGMENTATION, LACUNAR INFARCTS, QUANTIFICATION
  • Akdeniz University Affiliated: Yes

Abstract

Brain lesions are life threatening diseases. Traditional diagnosis of brain lesions is performed visually by neuro-radiologists. Nowadays, advanced technologies and the progress in magnetic resonance imaging provide computer aided diagnosis using automated methods that can detect and segment abnormal regions from different medical images. Among several techniques, machine learning based methods are flexible and efficient. Therefore, in this paper, we present a review on techniques applied for detection and segmentation of brain lesions from magnetic resonance images with supervised and unsupervised machine learning techniques.