School of Communication and Information Engineering, Shanghai University, Shanghai, China.
Department of Histology and Embryology, Medical College, Nantong University, Nantong, China.
Comput Med Imaging Graph. 2015 Apr;41:61-6. doi: 10.1016/j.compmedimag.2014.06.002. Epub 2014 Jun 8.
Although color medical images are important in clinical practice, they are usually converted to grayscale for further processing in pattern recognition, resulting in loss of rich color information. The sparse coding based linear spatial pyramid matching (ScSPM) and its variants are popular for grayscale image classification, but cannot extract color information. In this paper, we propose a joint sparse coding based SPM (JScSPM) method for the classification of color medical images. A joint dictionary can represent both the color information in each color channel and the correlation between channels. Consequently, the joint sparse codes calculated from a joint dictionary can carry color information, and therefore this method can easily transform a feature descriptor originally designed for grayscale images to a color descriptor. A color hepatocellular carcinoma histological image dataset was used to evaluate the performance of the proposed JScSPM algorithm. Experimental results show that JScSPM provides significant improvements as compared with the majority voting based ScSPM and the original ScSPM for color medical image classification.
虽然彩色医学图像在临床实践中很重要，但它们通常会被转换为灰度以便在模式识别中进行进一步处理，从而导致丰富的颜色信息丢失。基于稀疏编码的线性空间金字塔匹配（ScSPM）及其变体在灰度图像分类中很受欢迎，但无法提取颜色信息。在本文中，我们提出了一种联合稀疏编码的 SPM（JScSPM）方法，用于彩色医学图像的分类。联合字典可以表示每个颜色通道中的颜色信息以及通道之间的相关性。因此，从联合字典计算出的联合稀疏码可以携带颜色信息，因此该方法可以轻松地将最初为灰度图像设计的特征描述符转换为颜色描述符。使用彩色肝细胞癌组织学图像数据集评估了所提出的 JScSPM 算法的性能。实验结果表明，与基于多数投票的 ScSPM 和原始 ScSPM 相比，JScSPM 为彩色医学图像分类提供了显著的改进。
