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
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Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
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ISSN:2168-2194
2168-2208
2168-2208
DOI:10.1109/JBHI.2014.2360515