基于支持向量机的MRI脑分类

M. Othman, N. Abdullah, Nurul Fazrena Kamal
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引用次数: 94

摘要

在短时间内,随着对自动化和高效诊断需求的增加,医学成像领域变得越来越重要。除此之外,医学图像检索系统是为放射科医师提供检索与查询图像内容相似的图像的工具。磁共振成像(MRI)是一种研究大脑图像的成像技术,在神经科学研究中发挥了重要作用。分类是检索系统中区分正常患者和可能有异常或肿瘤的患者的重要组成部分。本文利用离散小波变换获得了与MRI图像相关的特征。采用基于核的支持向量机(SVM)等先进技术对MRI数据体进行正常和异常分类。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
MRI brain classification using support vector machine
The field of medical imaging gains its importance with increase in the need of automated and efficient diagnosis in a short period of time. Other than that, medical image retrieval system is to provide a tool for radiologists to retrieve the images similar to query image in content. Magnetic resonance imaging (MRI) is an imaging technique that has played an important role in neuroscience research for studying brain images. Classification is an important part in retrieval system in order to distinguish between normal patients and those who have the possibility of having abnormalities or tumor. In this paper, we have obtained the feature related to MRI images using discrete wavelet transformation. An advanced kernel based techniques such as Support Vector Machine (SVM) for the classification of volume of MRI data as normal and abnormal will be deployed.
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