Abstract
Background: Recent breakthroughs in computer vision and digital microscopy have prompted the application of such technologies in cancer diagnosis, especially in histopathological image analysis. Earlier, an attempt to classify hepatocellular carcinoma images based on nuclear and structural features has been carried out on a set of surgical resected samples. Here, we proposed methods to enhance the process and improve the classification performance. Methods: First, we segmented the histological components of the liver tissues and generated several masked images. By utilizing the masked images, some set of new features were introduced, producing three sets of features consisting nuclei, trabecular and tissue changes features. Furthermore, we extended the classification process by using biopsy resected samples in addition to the surgical samples. Results: Experiments by using support vector machine (SVM) classifier with combinations of features and sample types showed that the proposed methods improve the classification rate in HCC detection for about 1-3%. Moreover, detection rate of low-grades cancer increased when the new features were appended in the classification process, although the rate was worsen in the case of undifferentiated tumors. Conclusions: The masking process increased the reliability of extracted nuclei features. The additional of new features improved the system especially for early HCC detection. Likewise, the combination of surgical and biopsy samples as training data could also improve the classification rates. Therefore, the methods will extend the support for pathologists in the HCC diagnosis.
Highlights
Hepatocellular carcinoma (HCC) is a malignant tumor with hepatocellular differentiation and one of the most common cancers in the world
The system provides pathologists with the quantitative measurement of tissue morphology using a digital slide of hematoxylin-eosin (HE) stained liver tissue specimen, as well as the HCC detection based on those measurement results
Kiyuna et al [3] had introduced an automatic classification of HCC images based on 13 types of nuclear and structural features, where each feature consists of 6 statistical distributions
Summary
Hepatocellular carcinoma (HCC) is a malignant tumor with hepatocellular differentiation and one of the most common cancers in the world. This type of cancer is often diagnosed when the survival time is measured in months causing high death rates [1]. The system provides pathologists with the quantitative measurement of tissue morphology using a digital slide of hematoxylin-eosin (HE) stained liver tissue specimen, as well as the HCC detection based on those measurement results. In order to improve the classification performance, we have developed methods to segment the liver tissue and quantify additional tissue features such as trabecular morphology [4]. This paper reports the evaluation results on the impact of the segmentation and the additional features in the HCC detection performance
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