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基于深度卷積神經(jīng)網(wǎng)絡的低劑量CT肺部去噪

呂曉琪 吳涼 谷宇 張明 李菁

呂曉琪, 吳涼, 谷宇, 張明, 李菁. 基于深度卷積神經(jīng)網(wǎng)絡的低劑量CT肺部去噪[J]. 電子與信息學報, 2018, 40(6): 1353-1359. doi: 10.11999/JEIT170769
引用本文: 呂曉琪, 吳涼, 谷宇, 張明, 李菁. 基于深度卷積神經(jīng)網(wǎng)絡的低劑量CT肺部去噪[J]. 電子與信息學報, 2018, 40(6): 1353-1359. doi: 10.11999/JEIT170769
Lü Xiaoqi, WU Liang, GU Yu, ZHANG Ming, LI Jing. Low Dose CT Lung Denoising Model Based on Deep Convolution Neural Network[J]. Journal of Electronics & Information Technology, 2018, 40(6): 1353-1359. doi: 10.11999/JEIT170769
Citation: Lü Xiaoqi, WU Liang, GU Yu, ZHANG Ming, LI Jing. Low Dose CT Lung Denoising Model Based on Deep Convolution Neural Network[J]. Journal of Electronics & Information Technology, 2018, 40(6): 1353-1359. doi: 10.11999/JEIT170769

基于深度卷積神經(jīng)網(wǎng)絡的低劑量CT肺部去噪

doi: 10.11999/JEIT170769
基金項目: 

國家自然科學基金(61771266, 61179019),內蒙古自治區(qū)自然科學基金(2015MS0604),包頭市科技計劃項目(2015C2006-14),內蒙古自治區(qū)高等學??茖W研究項目(NJZY145)

Low Dose CT Lung Denoising Model Based on Deep Convolution Neural Network

Funds: 

The National Natural Science Foundation of China (61771266, 61179019), The Natural Science Foundation of the Inner Mongolia Autonomous region (2015MS0604), The Science and Technology Plan Projects of Baotou City (2015C2006-14), The Institutions of Higher Learning Scientific Research Projects of the Inner Mongolia Autonomous region (NJZY145)

  • 摘要: 為了降低低劑量CT肺部噪聲對肺癌篩查后期診斷的影響,該文提出一種基于深度卷積神經(jīng)網(wǎng)絡的低劑量CT肺部去噪算法。以完整的CT肺部圖像作為輸入,池化層對輸入圖像進行降維處理;批規(guī)范化解決隨著網(wǎng)絡深度的增加性能降低的問題;引入殘差學習,學習模型中每一層的殘差,最后輸出去噪圖像。與經(jīng)典去噪算法實驗結果對比,所提方法在解決去噪方面達到了很好的濾波效果,同時也較好地保留了肺部圖像的細節(jié)信息,大大優(yōu)于傳統(tǒng)的去噪算法。
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  • 文章訪問數(shù):  2027
  • HTML全文瀏覽量:  308
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  • 被引次數(shù): 0
出版歷程
  • 收稿日期:  2017-08-01
  • 修回日期:  2017-12-01
  • 刊出日期:  2018-06-19

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