Segmentation of Tumor and Edema Along With Healthy Tissues of Brain Using Wavelets and Neural Networks

DEMİRHAN A., Toru M., GÜLER İ.

IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, cilt.19, sa.4, ss.1451-1458, 2015 (SCI-Expanded) identifier identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 19 Sayı: 4
  • Basım Tarihi: 2015
  • Doi Numarası: 10.1109/jbhi.2014.2360515
  • Dergi Adı: IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.1451-1458
  • Anahtar Kelimeler: Brain magnetic resonance (MR), image segmentation, learning vector quantization (LVQ), self-organizing feature map, stationary wavelet transform (SWT), MR-IMAGES, TEXTURE CLASSIFICATION, AUTOMATED SEGMENTATION, FRAMEWORK
  • Gazi Üniversitesi Adresli: Evet

Özet

Robust brain magnetic resonance (MR) segmentation algorithms are critical to analyze tissues and diagnose tumor and edema in a quantitative way. In this study, we present a new tissue segmentation algorithm that segments brain MR images into tumor, edema, white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF). The detection of the healthy tissues is performed simultaneously with the diseased tissues because examining the change caused by the spread of tumor and edema on healthy tissues is very important for treatment planning. We used T1, T2, and FLAIR MR images of 20 subjects suffering from glial tumor. We developed an algorithm for stripping the skull before the segmentation process. The segmentation is performed using self-organizing map (SOM) that is trained with unsupervised learning algorithm and fine-tuned with learning vector quantization (LVQ). Unlike other studies, we developed an algorithm for clustering the SOM instead of using an additional network. Input feature vector is constructed with the features obtained from stationary wavelet transform (SWT) coefficients. The results showed that average dice similarity indexes are 91% for WM, 87% for GM, 96% for CSF, 61% for tumor, and 77% for edema.