GEOMETRIC ANALYSIS OF STRUCTURAL CHANGES IN MICROSCOPIC NUCLEI IMAGES FOR DRUG-INDUCED CYTOTOXIC ASSESSMENT

Abstract

Investigation of drug-induced structural changes in cell lines at different concentrations using microscopic images is essential to understand their cytotoxic effects. In this study, geometric shape descriptors to evaluate the toxicity effects of a particular drug in cell images are formulated. For this, fluorescence microscopic images of drug-untreated and drug-treated mouse cardiac muscle HL1 cells are considered. Ratiometric index of cellular to non-cellular area and, Zernike moment measures are calculated for three different thresholds at different drug concentrations namely 0.6, 1.2, 2.5, 5, and 10[Formula: see text][Formula: see text]M. Statistical analysis is performed to find the significant features. Classification is performed using Support Vector Machine (SVM) to differentiate drug untreated with treated cells at different concentrations. Results demonstrate that the proposed features are able to characterize the shape variations in cell images at different concentrations, and validates the efficacy of segmentation. Mean cellular area ratio is found to decrease from drug-untreated to drug-treated at various concentrations. Significant shape alterations in cellular structures are also obtained using Zernike moment measures for these cases. The machine learning approach using SVM provides better performance in classifying the drug untreated with progressively increasing drug concentrations. Hence, the proposed pipeline of methods could be clinically used to determine the maximum permissible drug tolerance levels during the development of new drugs.