IEEE J Biomed Health Inform - Segmentation of tumor and edema along with healthy tissues of brain using wavelets and neural networks.

Tópicos

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Resumo

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.

Resumo Limpo

robust brain magnet reson mr segment algorithm critic analyz tissu diagnos tumor edema quantit way studi present new tissu segment algorithm segment brain mr imag tumor edema white matter wm gray matter gm cerebrospin fluid csf detect healthi tissu perform simultan diseas tissu examin chang caus spread tumor edema healthi tissu import treatment plan use t t flair mr imag subject suffer glial tumor develop algorithm strip skull segment process segment perform use selforgan map som train unsupervis learn algorithm finetun learn vector quantiz lvq unlik studi develop algorithm cluster som instead use addit network input featur vector construct featur obtain stationari wavelet transform swt coeffici result show averag dice similar index wm gm csf tumor edema

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