Pattern Recognition Research Group, Center for Artificial Intelligence Technology, Faculty of Information Science and Technology, University Kebangsaan Malaysia, 43600 Bangi, Malaysia.
Computer Science Department, Prince Abdullah Bin Ghazi Faculty of Information Technology, Al-Balqa Applied University, Jordan.
Artif Intell Med. 2018 May;87:78-90. doi: 10.1016/j.artmed.2018.04.002. Epub 2018 Apr 19.
Feature selection (FS) methods are widely used in grading and diagnosing prostate histopathological images. In this context, FS is based on the texture features obtained from the lumen, nuclei, cytoplasm and stroma, all of which are important tissue components. However, it is difficult to represent the high-dimensional textures of these tissue components. To solve this problem, we propose a new FS method that enables the selection of features with minimal redundancy in the tissue components.
We categorise tissue images based on the texture of individual tissue components via the construction of a single classifier and also construct an ensemble learning model by merging the values obtained by each classifier. Another issue that arises is overfitting due to the high-dimensional texture of individual tissue components. We propose a new FS method, SVM-RFE(AC), that integrates a Support Vector Machine-Recursive Feature Elimination (SVM-RFE) embedded procedure with an absolute cosine (AC) filter method to prevent redundancy in the selected features of the SV-RFE and an unoptimised classifier in the AC.
We conducted experiments on H&E histopathological prostate and colon cancer images with respect to three prostate classifications, namely benign vs. grade 3, benign vs. grade 4 and grade 3 vs. grade 4. The colon benchmark dataset requires a distinction between grades 1 and 2, which are the most difficult cases to distinguish in the colon domain. The results obtained by both the single and ensemble classification models (which uses the product rule as its merging method) confirm that the proposed SVM-RFE(AC) is superior to the other SVM and SVM-RFE-based methods.
We developed an FS method based on SVM-RFE and AC and successfully showed that its use enabled the identification of the most crucial texture feature of each tissue component. Thus, it makes possible the distinction between multiple Gleason grades (e.g. grade 3 vs. grade 4) and its performance is far superior to other reported FS methods.
特征选择（FS）方法广泛应用于前列腺组织病理学图像的分级和诊断。在此背景下，FS 是基于从管腔、细胞核、细胞质和基质中提取的纹理特征，这些都是重要的组织成分。然而，这些组织成分的高维纹理很难被准确地表示出来。为了解决这个问题，我们提出了一种新的 FS 方法，能够在组织成分中选择具有最小冗余的特征。
我们通过构建单个分类器对组织图像进行分类，根据单个组织成分的纹理进行分类，同时通过合并每个分类器的结果来构建一个集成学习模型。另一个出现的问题是由于单个组织成分的高维纹理而导致的过拟合。我们提出了一种新的 FS 方法，即 SVM-RFE(AC)，它将支持向量机-递归特征消除（SVM-RFE）嵌入过程与绝对余弦（AC）滤波器方法相结合，以防止 SV-RFE 选择的特征和 AC 中的未优化分类器的冗余。
我们在 H&E 前列腺和结肠癌组织学图像上进行了实验，涉及三种前列腺分类，即良性与 3 级、良性与 4 级以及 3 级与 4 级。结肠基准数据集需要区分 1 级和 2 级，这是在结肠领域中最难区分的情况。单个和集成分类模型（使用乘积规则作为合并方法）的结果都证实了所提出的 SVM-RFE(AC)优于其他基于 SVM 和 SVM-RFE 的方法。
我们开发了一种基于 SVM-RFE 和 AC 的 FS 方法，并成功地表明它能够识别每个组织成分中最重要的纹理特征。因此，它可以区分多个 Gleason 等级（例如 3 级与 4 级），其性能远远优于其他报告的 FS 方法。
