Intelligent technique for CT brain image segmentation

P. Mallick, B. S. Satapathy, M. Mohanty, S. S. Kumar
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引用次数: 8

Abstract

Image segmentation plays a vital role in medical imaging applications. It facilitates the delineation of anatomical structures and other regions of interest. Magnetic resonance imaging (MRI), computed tomography (CT), digital mammography, and other imaging modalities provide an effective means for noninvasively mapping the anatomy of a subject. These technologies have greatly increased knowledge of normal and diseased anatomy for medical research and are a critical component in diagnosis and treatment planning. In this paper, the brain image is considered for analysis and detection. Initially the region of interest is found, that helps to detect the particular content of the image and set the boundary of it. Fuzzy based clustering method is applied as an intelligent method for the image segmentation. For this purpose the thresholding using histogram is done. The final results are also compared for different clustering algorithms.
CT脑图像的智能分割技术
图像分割在医学成像应用中起着至关重要的作用。它有助于描绘解剖结构和其他感兴趣的区域。磁共振成像(MRI)、计算机断层扫描(CT)、数字乳房x线照相术和其他成像方式提供了一种有效的方法来绘制受试者的无创解剖图。这些技术大大增加了医学研究的正常和病变解剖知识,是诊断和治疗计划的关键组成部分。本文采用脑图像进行分析和检测。首先找到感兴趣的区域,这有助于检测图像的特定内容并设置其边界。将模糊聚类方法作为一种智能的图像分割方法。为此,使用直方图进行阈值分割。最后对不同聚类算法的聚类结果进行了比较。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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