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, Amerika Birleşik Devletleri, 18 - 20 Aralık 2016, ss.582-587 identifier identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Doi Numarası: 10.1109/icmla.2016.192
  • Basıldığı Şehir: California
  • Basıldığı Ülke: Amerika Birleşik Devletleri
  • Sayfa Sayıları: ss.582-587
  • Anahtar Kelimeler: Brain tumor, brain lesion, lacunar infarct, machine learning MRI, neural networks, WHITE-MATTER HYPERINTENSITIES, SMALL VESSEL DISEASE, AUTOMATIC SEGMENTATION, LACUNAR INFARCTS, QUANTIFICATION
  • Akdeniz Üniversitesi Adresli: Evet

Özet

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.