Multi-Features Classification of Prostate Carcinoma Observed in Histological Sections: Analysis of Wavelet-Based Texture and Colour Features
Multi-Features Classification of Prostate Carcinoma Observed in Histological Sections: Analysis of Wavelet-Based Texture and Colour Features
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DOI：
10.3390/cancers11121937
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发表时间：
2019-12-01
期刊：
影响因子：
5.2
通讯作者：
Choi, Heung-Kook
中科院分区：
文献类型：
--
作者：
Bhattacharjee, Subrata;Kim, Cho-Hee;Choi, Heung-Kook
显微活检图像本质上是彩色的，因为病理学家使用苏木精和曙红化学彩色染料进行活检检查。在本研究中，活检图像用于良性和恶性前列腺组织的组织学分级和分析。本研究分析了以下 PCa 分级：良性、3 级、4 级和 5 级。活检成像对于 PCa 的临床评估变得越来越重要。为了对前列腺癌的组织学分级进行分析和分类，采用基于像素的颜色矩描述符（PCMD）和灰度共生矩阵（GLCM）方法提取最显着的特征进行多层感知器（MLP）神经网络分类。进行Haar小波变换提取GLCM纹理特征，并从前列腺组织的RGB（红/绿/蓝）彩色图像中提取颜色特征。使用 R 编程语言进行 MANOVA 统计测试，以根据 F 值和 P 值选择显着特征。使用 1 级小波纹理和颜色特征，我们获得了 92.7% 的平均最高准确率。 MLP 分类器表现良好，我们的研究显示了基于前列腺癌组织学切片的多特征分类的有希望的结果。
Microscopic biopsy images are coloured in nature because pathologists use the haematoxylin and eosin chemical colour dyes for biopsy examinations. In this study, biopsy images are used for histological grading and the analysis of benign and malignant prostate tissues. The following PCa grades are analysed in the present study: benign, grade 3, grade 4, and grade 5. Biopsy imaging has become increasingly important for the clinical assessment of PCa. In order to analyse and classify the histological grades of prostate carcinomas, pixel-based colour moment descriptor (PCMD) and gray-level co-occurrence matrix (GLCM) methods were used to extract the most significant features for multilayer perceptron (MLP) neural network classification. Haar wavelet transformation was carried out to extract GLCM texture features, and colour features were extracted from RGB (red/green/blue) colour images of prostate tissues. The MANOVA statistical test was performed to select significant features based on F-values and P-values using the R programming language. We obtained an average highest accuracy of 92.7% using level-1 wavelet texture and colour features. The MLP classifier performed well, and our study shows promising results based on multi-feature classification of histological sections of prostate carcinomas.