Quantitative analysis of liver tumors at different stages using microscopic hyperspectral imaging technology

Jiansheng Wang; Qingli Li

doi:10.1117/1.JBO.23.10.106002

1 October 2018 Quantitative analysis of liver tumors at different stages using microscopic hyperspectral imaging technology

Jiansheng Wang, Qingli Li

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Journal of Biomedical Optics, Vol. 23, Issue 10, 106002 (October 2018). https://doi.org/10.1117/1.JBO.23.10.106002

Abstract

Liver cancer has one of the highest rates of human morbidity and mortality. However, in terms of pathology, liver cancer is traditionally clinically diagnosed based on observation of microscopic images of pathological liver sections. This paper investigates in vitro samples of rat models of bile duct carcinoma and presents a quantitative analysis method based on microscopic hyperspectral imaging technology to evaluate liver cancers at different stages. The example-based feature extraction method used in this paper mainly includes two algorithms: a morphological watershed algorithm is applied to find object and segment pathological components of pathological liver sections at different stages, and a support vector machine algorithm is implemented for liver tumor classification. Majority/minority analysis is utilized as the postclassification tool to eliminate small plaques from the preliminary classification results. Then, pseudocolor synthesis in RGB color space is used to produce the final results. The experimental results show that this method can effectively calculate the percent tumor areas in liver biopsies at different time points, that is, 3.338%, 11.952%, 15.125%, and 23.375% at 8, 12, 16, and 20 weeks, respectively. Notably, through tracking analysis, the processed results of 8-week images showed the possibility for early diagnosis of the liver tumor.

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Jiansheng Wang and Qingli Li "Quantitative analysis of liver tumors at different stages using microscopic hyperspectral imaging technology," Journal of Biomedical Optics 23(10), 106002 (1 October 2018). https://doi.org/10.1117/1.JBO.23.10.106002

Received: 15 April 2018; Accepted: 13 September 2018; Published: 1 October 2018

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