Nuclear density and nuclear morphometric features such as shape of nuclei are useful in diagnosing hepato-cellular carcinoma, especially well-differentiated hepatocellular carcinoma (ewHCC). We previously developed a support system for diagnosing ewHCC that enables the user to estimate the nuclear density and the roundness factor of nuclei. The system automatically extracts the positions and contours of nuclei from a microscopic image. However, it takes a few minutes for the user to correct wrong positions and contours using a graphical user interface (GUI). Our target is to improve the accuracy for nuclear position extraction and contours extraction and to make the system more convenient. As a method to improve the accuracy, a multimodal method was employed. A multimodal method is a method to use images captured by more than one imaging methods. Dark-field and phase-contrast images, which can easily be captured using a combined condenser and a phasecontrast objective lens, were used in addition to a conventional bright-field image. Three kinds of multimodal techniques, alpha-blending of bright-field and dark-field images, enhancement of nuclei, and removal of positions extracted in sinusoidal areas, were employed. Experiments for nuclear position extraction and contour extraction were carried out. The best results were obtained when all the multimodal techniques were employed, showing the effectiveness of the method.