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

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 cer...

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Veröffentlicht in:	IEEE journal of biomedical and health informatics Jg. 19; H. 4; S. 1451 - 1458
Hauptverfasser:	Demirhan, Ayse, Toru, Mustafa, Guler, Inan
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States IEEE 01.07.2015 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Accuracy Algorithms Artificial neural networks Brain Brain - pathology Brain MR Brain Neoplasms - pathology Databases, Factual Edema Edema - pathology Feature extraction Humans Image Processing, Computer-Assisted - methods Image segmentation Learning learning vector quantization Magnetic Resonance Imaging - methods Medical services Neural Networks, Computer Segmentation self-organizing feature map stationary wavelet transform Training Tumors Vectors Wavelet Analysis Wavelet transforms self-organizing feature map Brain magnetic resonance (MR) image segmentation stationary wavelet transform (SWT) learning vector quantization (LVQ)
ISSN:	2168-2194, 2168-2208, 2168-2208
Online-Zugang:	Volltext
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