利用高效编码对乳腺 X 光片中的乳腺组织进行分类。
Classification of breast tissue in mammograms using efficient coding.
机构信息
Laboratory of Biological Information Processing, Department of Electrical Engineering, Federal University of Maranhão - UFMA, São Luís, MA, Brazil.
出版信息
Biomed Eng Online. 2011 Jun 24;10:55. doi: 10.1186/1475-925X-10-55.
BACKGROUND
Female breast cancer is the major cause of death by cancer in western countries. Efforts in Computer Vision have been made in order to improve the diagnostic accuracy by radiologists. Some methods of lesion diagnosis in mammogram images were developed based in the technique of principal component analysis which has been used in efficient coding of signals and 2D Gabor wavelets used for computer vision applications and modeling biological vision.
METHODS
In this work, we present a methodology that uses efficient coding along with linear discriminant analysis to distinguish between mass and non-mass from 5090 region of interest from mammograms.
RESULTS
The results show that the best rates of success reached with Gabor wavelets and principal component analysis were 85.28% and 87.28%, respectively. In comparison, the model of efficient coding presented here reached up to 90.07%.
CONCLUSIONS
Altogether, the results presented demonstrate that independent component analysis performed successfully the efficient coding in order to discriminate mass from non-mass tissues. In addition, we have observed that LDA with ICA bases showed high predictive performance for some datasets and thus provide significant support for a more detailed clinical investigation.
背景
在西方国家，女性乳腺癌是癌症死亡的主要原因。计算机视觉领域已经做出了一些努力，以提高放射科医生的诊断准确性。一些基于主成分分析的乳腺图像病变诊断方法已经被开发出来，该技术用于信号的有效编码，二维 Gabor 小波用于计算机视觉应用和生物视觉建模。
方法
在这项工作中，我们提出了一种使用有效编码和线性判别分析的方法，以区分乳腺中肿块和非肿块区域的 5090 个感兴趣区域。
结果
结果表明，使用 Gabor 小波和主成分分析的最佳成功率分别为 85.28%和 87.28%。相比之下，这里提出的有效编码模型最高可达 90.07%。
结论
总的来说，所呈现的结果表明，独立成分分析成功地进行了有效编码，以区分肿块和非肿块组织。此外，我们观察到基于 ICA 的 LDA 对某些数据集具有较高的预测性能，因此为更详细的临床研究提供了重要支持。
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