论文标题
使用生态多样性度量和离散小波转换的组织病理学图像的质地表征
Texture Characterization of Histopathologic Images Using Ecological Diversity Measures and Discrete Wavelet Transform
论文作者
论文摘要
乳腺癌是主要影响女性人群的健康问题。早期发现增加了有效治疗的机会，从而改善了疾病的预后。在这方面，已经提出了计算工具来帮助专家解释乳房数字图像检查，从而为检测和诊断肿瘤和癌细胞提供了特征。但是，检测具有高灵敏度率并降低假阳性率的肿瘤仍然具有挑战性。纹理描述符在医学图像分析中非常流行，尤其是在组织病理学图像（HI）中，这是因为在此类图像中发现的纹理和由于染色过程中的不规则性而导致的组织外观的可变性。这种可变性可能取决于染色方案的差异，例如固定，染色条件上的不一致以及在实验室或同一实验室之间的试剂。鉴于此类图像的内在属性的分布形成了非确定的复杂系统，用于以判别方式量化HI信息的纹理特征提取具有挑战性。本文提出了一种以相当大的成功率来表征其纹理的方法。通过采用生态多样性度量和离散的小波变换，与最先进的方法相比，在两个HI数据集上具有有希望的精度，可以量化此类图像的内在特性。
Breast cancer is a health problem that affects mainly the female population. An early detection increases the chances of effective treatment, improving the prognosis of the disease. In this regard, computational tools have been proposed to assist the specialist in interpreting the breast digital image exam, providing features for detecting and diagnosing tumors and cancerous cells. Nonetheless, detecting tumors with a high sensitivity rate and reducing the false positives rate is still challenging. Texture descriptors have been quite popular in medical image analysis, particularly in histopathologic images (HI), due to the variability of both the texture found in such images and the tissue appearance due to irregularity in the staining process. Such variability may exist depending on differences in staining protocol such as fixation, inconsistency in the staining condition, and reagents, either between laboratories or in the same laboratory. Textural feature extraction for quantifying HI information in a discriminant way is challenging given the distribution of intrinsic properties of such images forms a non-deterministic complex system. This paper proposes a method for characterizing texture across HIs with a considerable success rate. By employing ecological diversity measures and discrete wavelet transform, it is possible to quantify the intrinsic properties of such images with promising accuracy on two HI datasets compared with state-of-the-art methods.
