Department of Computer Science, College of Computer and Information Sciences, King Saud University, P.O. Box 51178, Riyadh 11543, Saudi Arabia.
Comput Biol Med. 2010 Apr;40(4):392-401. doi: 10.1016/j.compbiomed.2010.02.003. Epub 2010 Feb 24.
Breast cancer is among the leading causes of death in women worldwide. Mammography is the most effective imaging method for detecting no-palpable early-stage breast cancer. Understanding the nature of data in mammography images is very important for developing a model that fits well the data. Statistical distributions are widely used on the modelling of the data. Gamma distribution is more suitable than Gaussian distribution for modelling the data in mammography images. In this paper, we will use Gamma distribution to model the data in mammography images. The histogram of images can be seen as a mixture of Gamma distributions. Thresholds are selected at the valleys of a multi-modal histogram. The estimation of thresholds is based on the statistical parameters of the histogram. The expectation-maximization technique with gamma distribution (EMTG) is therefore developed to estimate the statistical histogram parameters. The experimental results on mammography images using this technique showed improvement in the accuracy in detection of the fibro-glandular discs.
乳腺癌是全球女性死亡的主要原因之一。乳腺 X 线摄影是检测不可触及的早期乳腺癌最有效的成像方法。了解乳腺 X 线摄影图像中数据的性质对于开发适合数据的模型非常重要。统计分布广泛用于数据建模。与高斯分布相比，伽马分布更适合对乳腺 X 线摄影图像中的数据进行建模。在本文中，我们将使用伽马分布对乳腺 X 线摄影图像中的数据进行建模。图像的直方图可以看作是伽马分布的混合。在多峰直方图的谷值处选择阈值。阈值的估计基于直方图的统计参数。因此，开发了具有伽马分布的期望最大化技术（EMTG）来估计统计直方图参数。该技术在乳腺 X 线摄影图像上的实验结果表明，在检测纤维-腺盘方面的准确性有所提高。
