Robust Estimation for Brain Tumor Segmentation

Prastawa, Marcel; Bullitt, Elizabeth; Ho, Sean; Gerig, Guido

doi:10.1007/978-3-540-39903-2_65

Cited by 35 publications

(21 citation statements)

References 16 publications

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“…Segmentation in two cases was performed by a new, automated method that uses multi-channel input and an atlas-based approach with five tissue classifiers: grey matter, white matter, CSF, edema, and tumor tissue [19]. Three tumor cases were segmented using a partially manual program that segments tumors via polygon drawing and filling on orthogonal cuts through an image volume.…”

Section: Image Acquisition and Segmentationmentioning

confidence: 99%

Vascular Attributes and Malignant Brain Tumors

Bullitt

Gerig

Aylward

et al. 2003

Lecture Notes in Computer Science

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Abstract. Many diseases affect blood vessel morphology. This report analyzes vessel attributes (tortuosity, vessel density, radius, and terminal branch count) within 5 malignant gliomas as seen by high-resolution MR. Results are compared to those in the same anatomical region of 14 normal controls. All tumor patients had marked increases in vessel tortuosity and terminal branch count. These results raise the interesting possibility of automatically defining "vessels of malignancy" within regions of interest on medical images.

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Section: Image Acquisition and Segmentationmentioning

confidence: 99%

Vascular Attributes and Malignant Brain Tumors

Bullitt

Gerig

Aylward

et al. 2003

Lecture Notes in Computer Science

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“…Many segmentation algorithms are presented in literature [6], [7], [8], [9], [10]. Among these techniques, medical image segmentation based on K-Means is mostly utilized [5].…”

Section: Fuzzy C-means Clustering Algorithmmentioning

confidence: 99%

Segmentation of Brain MR Images based on Finite Skew Gaussian Mixture Model with Fuzzy CMeans Clustering and EM Algorithm

Vadaparthi¹,

Srinivas²,

Suresh³

et al. 2011

IJCA

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Segmentation is a process of converting inhomogeneous data into homogeneous data. There are many segmentation techniques available inthe literature. Among these techniques, finite Gaussian Mixture Model using EM algorithm is one mostly used. However, Gaussian Mixture Model is suited well when the image under consideration is symmetric. But in reality, medical images are asymmetric. Hence, it is needed to develop new algorithms for segmenting non -symmetric images. Therefore, skew symmetric mixture model is utilized for this purpose. The segmentation is carried out by using Fuzzy C-Means clustering technique and the updated parameters are obtained through EM algorithm. The model is tested with 8 images and the segmentation evaluation is carried out by using objective evaluation criteria namely Jaccard Coefficient (JC) and Volumetric Similarity (VS), Variation of Information (VOI), Global Consistency Error (GCE) and Probabilistic Rand Index (PRI). The performance evaluation of reconstructed images is carried out by using image quality metrics. The experimentation is carried out using T 1 weighted images and the results are compared with the existing models.

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“…Different prevalence adjusted discrepancy measures were introduced during the last century, of which Dice similarity coefficient (DSC) [14] is mentioned most frequently. Prastawa et al [15] validated brain segmentation results using DSC together with surface distances, using segmentation validation tool VALMET [16]. Zou et al [17,18] proposed a logit transformation of Dice similarity coefficient for better statistical inference.…”

Section: Introductionmentioning

confidence: 99%

Statistical validation metric for accuracy assessment in medical image segmentation

et al. 2007

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Objective Validation of medical image segmentation algorithms is an open question, considering variance of individual pathologies and the related clinical requirements for accuracy. In this paper, we propose a validation metric capable to distinguish between an over and under-segmentation and account for different clinical applications. Materials and methods In this paper, we propose a validation metric representing a tradeoff between sensitivity and specificity. The metric has an advantage of differentiating between an over or under-segmentation which is an important feature for validating large sets of segmentation results, as human inspection is exhausting and time consuming. Although it is oriented to the accuracy measurement it is also closely related to the robustness of a method. Results Features of the metrics are analyzed alongside their medical impact. A set of numerical simulations is performed in order to compare the proposed metric with standardly used discrepancy measures. The metric is illustrated with a clinical case study, presenting accuracy assessment of an algorithm for calvarial tumor segmentation, validated on six patients.

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Robust Estimation for Brain Tumor Segmentation

Cited by 35 publications

References 16 publications

Vascular Attributes and Malignant Brain Tumors

Vascular Attributes and Malignant Brain Tumors

Segmentation of Brain MR Images based on Finite Skew Gaussian Mixture Model with Fuzzy CMeans Clustering and EM Algorithm

Statistical validation metric for accuracy assessment in medical image segmentation

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