计算机技术与发展Issue(1)：231-234,4.DOI:10.3969/j.issn.1673-629X.2014.01.059
基于L1范数主成分分析的颅脑图像恢复
Cerebral Image Recovery Based on L1-norm Principal Component Analysis
摘要
Abstract
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.关键词
脑图像恢复/主成分分析/L1范数/稀疏表示Key words
cerebral images recovery/principal component analysis/L1-norm/sparse representation分类
信息技术与安全科学引用本文复制引用
赵海峰,于雪敏,邹际祥,孙登第..基于L1范数主成分分析的颅脑图像恢复[J].计算机技术与发展,2014,(1):231-234,4.基金项目
国家自然科学基金资助项目(61073116,61272152,61003131) （61073116,61272152,61003131）
安徽省自然科学基金项目(1208085MF109) （1208085MF109）
