Coupling of Radial-Basis Network and Active Contour Model for Multispectral Brain MRI Segmentation

Valdés-Cristerna, Raquel; Médina-Bañuelos, Verónica; Yáñez-Suárez, Oscar

doi:10.1109/tbme.2003.820377

Cited by 35 publications

(18 citation statements)

References 28 publications

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“…As demonstrated in the following synthetic normal data experiments, the Tanimoto indexes of GM and WM measurements produced by the ICAþSVM method were higher than that using the SVM alone. Furthermore, the averaged Tanimoto indexes of GM and WM classification by the ICAþSVM was 0.79 for the synthetic image data with 3% noise level and 0% nonuniformity intensity, which was higher than those reported by other works in the literature (28). In addition to better classification accuracy, our results also showed a lower CV of intra-operator and inter-operator variabilities in Tanimoto indexes with a CV of 1.0 % and 0.4 % of GM and 1.3% and 2.6% of WM for the synthetic data with 3% noise level and 0% nonuniformity intensity.…”

Section: Effectiveness Of Ica1svm Methods With Higher Tanimoto Index Acontrasting

confidence: 58%

Quantitative analysis in clinical applications of brain MRI using independent component analysis coupled with support vector machine

Chai¹,

Chen²,

Chiang³

et al. 2010

Magnetic Resonance Imaging

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Purpose: To effectively perform quantification of brain normal tissues and pathologies simultaneously, independent component analysis (ICA) coupled with support vector machine (SVM) is investigated and evaluated for effective volumetric measurements of normal and lesion tissues using multispectral MR images. Materials and Methods:Synthetic and real MR data of normal brain and white matter lesion (WML) data were used to evaluate the accuracy and reproducibility of gray matter (GM), white matter (WM), and WML volume measurements by using the proposed ICAþSVM method to analyze three sets of MR images, T1-weighted, T2-weighted, and proton density/fluid-attenuated inversion recovery images.Results: The Tanimoto indexes of GM/WM classification in the normal synthetic data calculated by the ICAþSVM method were 0.82/0.89 for data with 0% noise level. As for clinical MR data experiments, the ICAþSVM method clearly extracted the normal tissues and white matter hyperintensity lesions from the MR images, with low intra-and inter-operator coefficient of variations. Conclusion:The experiments conducted provide evidence that the ICAþSVM method has shown promise and potential in applications to classification of normal and pathological tissues in brain MRI.

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Section: Effectiveness Of Ica1svm Methods With Higher Tanimoto Index Acontrasting

confidence: 58%

Quantitative analysis in clinical applications of brain MRI using independent component analysis coupled with support vector machine

Chai¹,

Chen²,

Chiang³

et al. 2010

Magnetic Resonance Imaging

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“…A combined radial basis function neural network (RBF) and contour model based MR image segmentation technique is used by [29]. A modified version of SOM with Markov random field model is suggested by [30].…”

Section: Selfmentioning

confidence: 99%

A Survey on Neural Network based Automatic Segmentation of Brain Magnetic Resonance Images

2015

IJRA

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Medical Images are used as an important tool for determination of Pathological condition of the vital organs of the body like brain, lungs, liver, etc. Segmentation is the first step towards automatic processing for analysis and evaluation of medical images. Especially, image segmentation is a prerequisite process for image content understanding in brain MRI for the development of a computer aided diagnosis (CAD) system. It is a technique, which partitions an image into units, which are homogeneous with respect to one or more characteristics. Automatic Segmentation of brain MRI is used as a diagnostic tool in neuro medicine. Abnormal growth of brain tissues can be detected with the help of segmentation techniques. Changes in volumetric growth of brain tissues such as White Matter (WM), Gray Matter (GM) andCerebrospinal fluid (CSF) can help in the early detection of neural disorders like epilepsy Alzheimer's disease, parenchymal bleeds, etc. Even though several automated methods are available for segmentation of Brain MRI, there is no clear differentiation between these techniques about the suitability for various neural disorders. We presented a review of the methods used in brain segmentation. The review covers imaging techniques, magnetic resonance imaging and methods for segmentation and how a known Neural Network with fixed structure and training procedure could be applied to resolve medical imaging problem pertaining to Brain image segmentation.

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“…Active contour adalah suatu kurva tertutup 2D yang dapat berdeformasi secara elastis pada bidang citra, dengan kemampuan berdeformasi ini karena pengaruh dari definisi gaya internal dan gaya eksternal yang harus diminimumkan oleh suatu fungsi objektif [4,[10][11][12] …”

Section: Metode Penelitianunclassified

Segmentasi Citra Secara Semi-Otomatis Untuk Visualisasi Volumetrik Citra Ct-Scan Pelvis

Suprijanto

Muchtadi

Setiawan

2010

MST

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AbstrakPerkembangan terkini dari perangkat pencitraan medik computerized tomography (CT) scan telah memungkinkan dihasilkannya citra dari penampang melintang secara multi irisan dalam orde beberapa detik. Citra medik digital yang dihasilkan merepresentasikan penampang melintang dari berbagai struktur jaringan dari irisan yang dicitrakan. Salah satu tantangan yang dapat membantu dalam proses diagnosis berbasis citra adalah ekstraksi informasi dari struktur anatomi tertentu dengan suatu metode segmentasi citra serta visualisasi volumetrik dengan bantuan komputer. Untuk kasus visualisasi volumetrik tulang pelvis pada citra CT-scan multi irisan, seluruh citra yang mengandung bagian struktur tulang pelvis harus disegmentasi. Pada penelitian ini, satu teknik segmentasi citra berbasis active contour akan diimplementasikan untuk melakukan segmentasi citra multi irisan secara semi otomatis. Proses segmentasi citra diawali dengan menentukan model kurva 2D yang dilakukan secara manual pada citra irisan pertama. Kemudian model kurva tersebut secara iterasi akan berdeformasi sampai dengan bentuk kurva yang berhimpit pada batas tepian citra tulang pelvis. Hari akhir kurva 2D pada irisan pertama akan digunakan sebagai inisialisasi model kurva 2D pada proses segmentasi citra irisan berikutnya. Proses tersebut akan berlanjut sampai dengan citra irisan terakhir. Metode segmentasi citra berbasis active contour akan dibandingkan dengan metode segmentasi secara nilai ambang dari homogenitas distribusi intensitas dan metode segmentasi secara manual. Analisis secara kualitatif terhadap hasil segmentasi tiap irisan dan analisis kualitatif pada representasi visualisasi volumetrik digunakan pada penelitian ini. AbstractSemi-Automatic Image Segmentation for Volumetric Visualization of Pelvis CT Scan-Images. The current development of computerized tomography (CT) has enable us to obtain cross sectional image using multi slicing techniques in an order of few seconds. The obtained images represent several tissue structures on cross section slice being imaged. One challenge to help diagnosis using CT images is extracting an anatomic structure of interest using a method of image segmentation and volumetric visualization with the assistance of computers. In case of volumetric visualization of pelvis bones extracted from multi-slice CT images, whole images which are containing part of pelvis bone structures must be segmented. In this research, an image segmentation technique based on active contour is implemented for semi-automatic multi slice image segmentation. Image segmentation steps are initialized with a define model of 2D curve on the first slice image manually. Next, its model curve is deformed to reach the final result of 2D curve that fits to boundary edges of pelvis bone image. The final result of 2D curve on previous slice image was used as an initialization model of 2D curve on the next slice images. This process will continue until the final slice image. This segmentation method was compared with the segmentation method based on th...

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Coupling of Radial-Basis Network and Active Contour Model for Multispectral Brain MRI Segmentation

Cited by 35 publications

References 28 publications

Quantitative analysis in clinical applications of brain MRI using independent component analysis coupled with support vector machine

Quantitative analysis in clinical applications of brain MRI using independent component analysis coupled with support vector machine

A Survey on Neural Network based Automatic Segmentation of Brain Magnetic Resonance Images

Segmentasi Citra Secara Semi-Otomatis Untuk Visualisasi Volumetrik Citra Ct-Scan Pelvis

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