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一种具有挖掘和形状先验分割过程的改进脑图像分类技术。

An improved brain image classification technique with mining and shape prior segmentation procedure.

机构信息

Department of Computer Science and Engineering, K. S. Rangasamy College of Technology, Tamilnadu, India.

出版信息

J Med Syst. 2012 Apr;36(2):747-64. doi: 10.1007/s10916-010-9542-8. Epub 2010 Jun 25.

Abstract

The shape prior segmentation procedure and pruned association rule with ImageApriori algorithm has been used to develop an improved brain image classification system are presented in this paper. The CT scan brain images have been classified into three categories namely normal, benign and malignant, considering the low-level features extracted from the images and high level knowledge from specialists to enhance the accuracy in decision process. The experimental results on pre-diagnosed brain images showed 97% sensitivity, 91% specificity and 98.5% accuracy. The proposed algorithm is expected to assist the physicians for efficient classification with multiple key features per image.

摘要

本文提出了一种改进的脑图像分类系统,该系统使用基于形状先验的分割过程和修剪关联规则的 ImageApriori 算法。考虑到从图像中提取的低级特征和专家提供的高级知识,将 CT 扫描脑图像分为正常、良性和恶性三类,以提高决策过程的准确性。对预诊断脑图像的实验结果表明,该算法具有 97%的灵敏度、91%的特异性和 98.5%的准确率。该算法有望帮助医生利用每张图像的多个关键特征进行高效分类。

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