Delineation of infarct lesions by Multi-dimensional Fuzzy C-Means of acute ischemic stroke patients

A. Subudhi, S. Jena, S. Sabut
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引用次数: 2

Abstract

Lesion size in diffusion weighted imaging (DWI) of magnetic resonance (MR) images is an important clinical parameter to assess the lesion area in ischemic stroke. Manual delineation of stroke lesion is time-consuming, highly user-dependent and difficult to perform in areas of indistinct borders. In this paper we present a segmentation process to detect lesion which separates non-enhancing brain lesion from healthy tissues in DWI MR images to aid in the task of tracking lesion area over time. Lesion segmentation by Fast Fuzzy C-means was performed in DWI images obtained from patients following ischemic stroke. The lesions are delineated and segmented by Multi- dimensional Fuzzy C-Means (FCM). A high visual similarity of lesions was observed in segmented images obtained by this method. The key elements are the accurate segmenting brain images from stroke patients and measuring the size of images in pixel-wise for defining areas with hypo- or hyper-intense signals. The relative area of the affected lesion is also measured with respect to normal brain image.
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急性缺血性脑卒中患者的多维模糊c -均值描述梗死灶
磁共振弥散加权成像(DWI)是评估缺血性脑卒中病变面积的重要临床参数。人工圈定脑卒中病灶耗时长,用户依赖性高,且难以在边界模糊的区域进行。在本文中,我们提出了一种检测病变的分割过程,该过程将DWI MR图像中的非增强脑病变与健康组织分开,以帮助跟踪病变区域。对缺血性脑卒中患者的DWI图像进行快速模糊c均值分割。病灶用多维模糊c均值(FCM)进行划定和分割。该方法获得的分割图像具有较高的视觉相似性。关键是准确分割中风患者的大脑图像,并测量图像的像素大小,以确定具有低或高强度信号的区域。受影响的病变的相对面积也被测量相对于正常的大脑图像。
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