本文選題：癲癇　切入點(diǎn)：張量　出處：《東北大學(xué)學(xué)報(bào)(自然科學(xué)版)》2017年07期 　論文類型：期刊論文

【摘要】：結(jié)合腦PET圖像信息,提出了一種基于張量子空間的半腦對(duì)稱度特征的識(shí)別方法用于識(shí)別PET圖像中癲癇病灶.首先計(jì)算全部腦PET圖像中所有體素的SUV,并基于SUV建立三階張量;然后提取半腦對(duì)稱度特征,建立半腦對(duì)稱度張量模型;其次利用多線性主成分分析(MPCA)方法對(duì)半腦對(duì)稱度張量模型進(jìn)行特征選擇;最后基于支持向量機(jī)(SVM)分類器進(jìn)行癲癇識(shí)別.實(shí)驗(yàn)結(jié)果表明:提出的算法能夠有效地識(shí)別腦PET圖像中的癲癇病灶,可以作為計(jì)算機(jī)輔助診斷方式幫助醫(yī)生進(jìn)行癲癇疾病的診斷.
[Abstract]:Based on the information of brain PET images, a method of recognition of hemispheres symmetry based on tensor quantum space is proposed to identify epileptic foci in PET images. Firstly, the sum of all voxels in all brain PET images is calculated, and the third-order Zhang Liang is established based on SUV. Then the hemispheres symmetry degree feature was extracted, and the Zhang Liang model of hemisencephalic symmetry degree was established. Secondly, the feature selection of the hemisencephalic symmetry degree Zhang Liang model was carried out by using the multi-linear principal component analysis (MPCA) method. The experimental results show that the proposed algorithm can effectively identify epileptic foci in brain PET images and can be used as a computer-aided diagnosis method to help doctors diagnose epilepsy.
【作者單位】： 東北大學(xué)軟件學(xué)院;東北大學(xué)中荷生物醫(yī)學(xué)與信息工程學(xué)院;
【基金】：國家自然科學(xué)基金資助項(xiàng)目(61472073)
【分類號(hào)】：R742.1;TP391.41

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