Multiwavelength light transmission imaging provides a possibility for early detection of breast cancer. However, due to strong scattering during the transmission process of breast tissue analysis, the transmitted image signal is weak and the image is blurred and this makes heterogeneous edge detection difficult. This paper proposes a method based on the weighted constraint decision (WCD) method to eliminate the erosion and checkerboard effects in image histogram equalization (HE) enhancement and to improve the recognition of heterogeneous edge. Multiwavelength transmission images of phantom are acquired on the designed experimental system and the mask image with high signal-to-noise ratio (SNR) is obtained by frame accumulation and an Otsu thresholding model. Then, during image enhancement the image is divided into low-gray-level (LGL) and high-gray-level (HGL) regions according to the distribution of light intensity in image. And the probability density distribution of gray level in the LGL and HGL regions are redefined respectively according to the WCD method. Finally, the reconstructed image is obtained based on the modified HE. The experimental results show that compared with traditional image enhancement methods, the WCD method proposed in this paper can greatly improve the contrast between heterogeneous region and normal region. Moreover, the correlation between the original image data is maintained to the greatest extent, so that the edge of the heterogeneity can be detected more accurately. In conclusion, the WCD method not only accurately identifies the edge of heterogeneity in multiwavelength transmission images, but it also could improve the clinical application of multiwavelength transmission images in the early detection of breast cancer.

中文翻译：

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EXPRESS：基于加权约束决策法的多波长传输图像异质边缘识别

多波长光透射成像为早期检测乳腺癌提供了可能。然而，由于乳腺组织分析在传输过程中散射较强，传输的图像信号较弱，图像模糊，使得异质边缘检测变得困难。本文提出了一种基于加权约束决策（WCD）方法的方法，以消除图像直方图均衡（HE）增强中的侵蚀和棋盘效应，提高对异质边缘的识别。在设计的实验系统上获取体模的多波长传输图像，并通过帧累积和Otsu阈值模型获得具有高信噪比（SNR）的掩模图像。然后，在图像增强过程中，根据图像中光强的分布，将图像分为低灰度（LGL）和高灰度（HGL）区域。并且根据WCD方法分别重新定义了LGL和HGL区域灰度的概率密度分布。最后，基于修改后的 HE 获得重构图像。实验结果表明，与传统的图像增强方法相比，本文提出的WCD方法可以大大提高异构区域与正常区域的对比度。并且最大程度地保持了原始图像数据之间的相关性，从而可以更准确地检测出异质性的边缘。综上所述，