Extraction of Brain Tumor from MR Images Using One-Class Support Vector Machine

Zhou, Jinyun; Chan, Kok Lim; Chong, Vincent; Krishnan, Sriram

doi:10.1109/iembs.2005.1615965

Cited by 76 publications

(49 citation statements)

References 9 publications

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“…Recently, both supervised and unsupervised segmentation methods for identification of brain tissue structures have been proposed. Automatic tissue or tumor segmentation based on multi-spectral data analysis (12,13), neural networking (14,15), support vector machines (16,17) and knowledge-based fuzzy c-means (FCM) clustering techniques (18 -20) all show great promise. The potential advantages of automatic tumor segmentation include removal of intra-and inter-observer variations, time efficiency and standardized criteria's for tumor characterization (18).…”

mentioning

confidence: 99%

Automatic glioma characterization from dynamic susceptibility contrast imaging: Brain tumor segmentation using knowledge‐based fuzzy clustering

Emblem

Nedregaard

Hald

et al. 2009

Magnetic Resonance Imaging

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Purpose:To assess whether glioma volumes from knowledge-based fuzzy c-means (FCM) clustering of multiple MR image classes can provide similar diagnostic efficacy values as manually defined tumor volumes when characterizing gliomas from dynamic susceptibility contrast (DSC) imaging. Materials and Methods:Fifty patients with newly diagnosed gliomas were imaged using DSC MR imaging at 1.5 Tesla. To compare our results with manual tumor definitions, glioma volumes were also defined independently by four neuroradiologists. Using a histogram analysis method, diagnostic efficacy values for glioma grade and expected patient survival were assessed. Results:The areas under the receiver operator characteristics curves were similar when using manual and automated tumor volumes to grade gliomas (P ϭ 0.576 -0.970). When identifying a high-risk patient group (expected survival Ͻ2 years) and a low-risk patient group (expected survival Ͼ2 years), a higher log-rank value from Kaplan-Meier survival analysis was observed when using automatic tumor volumes (14.403; P Ͻ 0.001) compared with the manual volumes (10.650 -12.761; P ϭ 0.001-0.002). Conclusion:Our results suggest that knowledge-based FCM clustering of multiple MR image classes provides a completely automatic, user-independent approach to selecting the target region for presurgical glioma characterization

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confidence: 99%

Automatic glioma characterization from dynamic susceptibility contrast imaging: Brain tumor segmentation using knowledge‐based fuzzy clustering

Emblem

Nedregaard

Hald

et al. 2009

Magnetic Resonance Imaging

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“…The other sub categories of classification based segmentation are supervised learning based segmentation and unsupervised learning based segmentation. Neural networks [14], support vector machine [15] are categorized as…”

Section: Related Workmentioning

confidence: 99%

Anisotropic Diffusion based Brain MRI Segmentation and 3D Reconstruction

Jaffar¹,

Zia²,

Latif³

et al. 2012

IJCIS

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In medical field visualization of the organs is very imperative for accurate diagnosis and treatment of any disease. Brain tumor diagnosis and surgery also required impressive 3D visualization of the brain to the radiologist. Detection and 3D reconstruction of brain tumors from MRI is a computationally time consuming and error-prone task. Proposed system detects and presents a 3D visualization model of the brain and tumor inside which greatly helps the radiologist to effectively diagnose and analyze the brain tumor. We proposed a multi-phase segmentation and visualization technique which overcomes the many problems of 3D volume segmentation methods like lake of fine details. In this system segmentation is done in three different phases which reduces the error chances. The system finds contours for skull, brain and tumor. These contours are stacked over and two novel methods are used to find the 3D visualization models. The results of these techniques, particularly of interpolation based, are impressive. Proposed system is tested against publically available data set [41] and MRI datasets available from MRI & CT center Rawalpindi, Pakistan [42].

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“…In the last decades, many methods have been proposed to segment the brain tumor of MR images, such as neural networks [13,14], support vector machine (SVM) [15], finite Gaussian mixture model [16], fuzzy C-means (FCM) [13,17], knowledge-based methods [18,19], atlas based method [20], active contour model [21], level set methods [22,23], and outlier detection [24]. Here, the segmentation task is regarded as a tissue recognition problem, which means using a well-trained model that can determine whether a pixel/ voxel belongs a normal or abnormal tissue.…”

Section: Introductionmentioning

confidence: 99%

A Neural Network-Based Method for Brain Abnormality Detection in MR Images Using Zernike Moments and Geometric Moments

Lashkari¹

2010

IJCA

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Nowadays, automatic defects detection in MR images is very important in many diagnostic and therapeutic applications. Because of high quantity data in MR images and blurred boundaries, tumor segmentation and classification is very hard. This paper has introduced one automatic brain tumor detection method to increase the accuracy and yield and decrease the diagnosis time. The goal is classifying the tissues to two classes of normal and abnormal. MR images that have been used here are MR images from normal and abnormal brain tissues. Here, it is tried to give clear description from brain tissues using Zernike Moments , Geometric Moment Invariants, energy, entropy, contrast and some other statistic features such as mean, median, variance, correlation, values of maximum and minimum intensity . It is used from a feature selection method to reduce the feature space too. this method uses from neural network to do this classification. The purpose of this project is to classify the brain tissues to normal and abnormal classes automatically, that saves the radiologist time, increases accuracy and yield of diagnosis.

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Extraction of Brain Tumor from MR Images Using One-Class Support Vector Machine

Cited by 76 publications

References 9 publications

Automatic glioma characterization from dynamic susceptibility contrast imaging: Brain tumor segmentation using knowledge‐based fuzzy clustering

Automatic glioma characterization from dynamic susceptibility contrast imaging: Brain tumor segmentation using knowledge‐based fuzzy clustering

Anisotropic Diffusion based Brain MRI Segmentation and 3D Reconstruction

A Neural Network-Based Method for Brain Abnormality Detection in MR Images Using Zernike Moments and Geometric Moments

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