本文選題：MR圖像 + 局部二值模式�。� 參考：《山東大學(xué)學(xué)報(bào)(工學(xué)版)》2017年02期

【摘要】：為解決磁共振(magnetic resonance,MR)腦部圖像來源不一以及病變位置和形態(tài)不固定造成MR腦部圖像分類精度不高的問題,提出基于局部二值模式(local binary pattern,LBP)的紋理特征提取,并用極限學(xué)習(xí)機(jī)(extreme learning machine,ELM)對(duì)M R圖像分類。計(jì)算圖像感興趣區(qū)域(region of interest,ROI)的掩碼,將圖像分成扇形的子區(qū)域,統(tǒng)計(jì)掩碼坐標(biāo)下各塊子區(qū)域的LBP直方圖,連接所有LBP直方圖作為特征向量通過ELM進(jìn)行分類。相比以前的方法,該方法能夠計(jì)算顱腦內(nèi)局部紋理特征,能分類來源不一以及多種病變的圖像。對(duì)腦部M R圖像分類進(jìn)行試驗(yàn),對(duì)所有樣本分類正確率超過92%,正類樣本正確率超過93%,負(fù)類樣本正確率超過91%。試驗(yàn)結(jié)果表明,該方法能夠?qū)^為復(fù)雜的MR圖像進(jìn)行正確分類。
[Abstract]:In order to solve the problem of low classification accuracy caused by the different sources of magnetic resonance MRI brain images and the location and shape of the lesions, a method of texture feature extraction based on local binary mode (local binary pattern LBP) is proposed to solve the problem that the classification accuracy of Mr images is not high due to the location and shape of the lesions. M R images were classified with extreme learning machine ELM. The mask of region of interest in the image is calculated, and the image is divided into sector subregions. The LBP histograms of each sub-region in the statistical mask coordinates are connected to all LBP histograms as feature vectors to be classified by ELM. Compared with the previous methods, this method can calculate the local texture features of the brain, and can classify images with different sources and various lesions. The classification accuracy of all samples is more than 92%, the correct rate of positive samples is more than 933%, and the accuracy rate of negative samples is more than 91%. The experimental results show that this method can correctly classify the more complicated Mr images.
【作者單位】： 桂林電子科技大學(xué)電子工程與自動(dòng)化學(xué)院;
【基金】：國(guó)家自然科學(xué)基金資助項(xiàng)目(61105004) 廣西高校圖像圖形智能處理重點(diǎn)實(shí)驗(yàn)室基金資助項(xiàng)目(LD16096X) 桂林電子科技大學(xué)創(chuàng)新基金資助項(xiàng)目(GDYCSZ201428)
【分類號(hào)】：R445.2;TP391.41

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本文編號(hào)：1905545