CAJ | 학술논문

医学颅脑图像处理已成为脑部疾病诊断的重要途径，为去除颅脑图像的噪声和异物遮挡而又不损失正常组织信息，提出了一种基于L1范数鲁棒主成分分析降维的颅脑图像恢复方法。首先用L1范数代替传统主成分分析中的L2范数，构造对噪声更加鲁棒的L1范数主成分分析；然后对其代价函数进行交替凸规划算法计算图像降维后的特征数据与投影矩阵；最后利用线性变换得到恢复后的医学颅脑图像。与传统图像压缩与恢复方法不同，该方法利用了L1范数的噪声鲁棒性，通过降维的方法来实现颅脑图像的恢复，同时实现去噪和异常检测的功能。在真实颅脑图像库中进行的比较实验证明了该方法对于颅脑图像恢复的有效性。
의학로뇌도상처리이성위뇌부질병진단적중요도경，위거제로뇌도상적조성화이물차당이우불손실정상조직신식，제출료일충기우L1범수로봉주성분분석강유적로뇌도상회복방법。수선용L1범수대체전통주성분분석중적L2범수，구조대조성경가로봉적L1범수주성분분석；연후대기대개함수진행교체철규화산법계산도상강유후적특정수거여투영구진；최후이용선성변환득도회복후적의학로뇌도상。여전통도상압축여회복방법불동，해방법이용료L1범수적조성로봉성，통과강유적방법래실현로뇌도상적회복，동시실현거조화이상검측적공능。재진실로뇌도상고중진행적비교실험증명료해방법대우로뇌도상회복적유효성。
As medical cerebral images have become an effective way of brain disease diagnosis,an efficient medical cerebral images recov-ery method based on L1 norm robust PCA dimensionality reduction is proposed to achieve denoising and anomaly detection with no loss of normal tissue information. First the L1 norm principal component analysis is constructed using L1 norm which is more robust to noise while in traditional principal component analysis it uses L2 norm. Then the characteristic data and the projection matrix are gotten by the alternate convex programming algorithm of the cost function. Finally,medical cerebral images after recovery are obtained by the linear transformation. Different from the traditional image compression and recovery method,the proposed method makes use of the robustness of the L1 norm. It realizes medical brain images recovery by dimension reduction,at the same time achieves denoising and anomaly detec-tion. The experimental results compared with the standard PCA algorithm in the real cerebral image database also prove the effectiveness of the proposed method for cerebral images recovery.