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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Vydané v:	IEEE journal of biomedical and health informatics Ročník 19; číslo 4; s. 1451 - 1458
Hlavní autori:	Demirhan, Ayse, Toru, Mustafa, Guler, Inan
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	United States IEEE 01.07.2015 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	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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Abstract	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.
AbstractList	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.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. 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.
Author	Guler, Inan Toru, Mustafa Demirhan, Ayse
Author_xml	– sequence: 1 givenname: Ayse surname: Demirhan fullname: Demirhan, Ayse email: ayseoguz@gazi.edu.tr organization: Fac. of Technol., Electron. & Comput. Technol., Ankara, Turkey – sequence: 2 givenname: Mustafa surname: Toru fullname: Toru, Mustafa email: mustafa.toru@29mayis.com.tr organization: Dept. of Radiol., 29 Mayis Hosp., Ankara, Turkey – sequence: 3 givenname: Inan surname: Guler fullname: Guler, Inan email: iguler@gazi.edu.tr organization: Fac. of Technol., Electron. & Comput. Technol., Ankara, Turkey
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SubjectTerms	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
Title	Segmentation of Tumor and Edema Along With Healthy Tissues of Brain Using Wavelets and Neural Networks
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