Image registration of biological tissue is essential for 3D reconstruction, which is important for visualizing and quantifying the 3D relationships between internal structures of an object. The biological role of DNA organization, which is an extremely complex 3D architecture within the cell nucleus, has come into focus since it has become clear that the chromatin structure in itself functions as a regulator of DNA. Thus, 3D reconstruction of cell nuclei based on consecutive series of high-resolution ultrathin slices may provide new information about the chromatin structure and its organizational changes during carcinogenesis. This work focuses mainly on the problem of registering successive serial transmission electron micrographs of ultrathin sections of mouse liver cell nuclei to analyse the 3D chromatin structure. A five-step semiautomatic interactive registration method is proposed. The first two steps of the procedure correct the rotation and translation components by using the phase correlation. The third, fourth and fifth steps correct the global distortion, employing a point mapping method based on different ways of selecting the control points. In step three, the control points were automatically computed by phase correlating corresponding subimages of the reference and sensed image. A semiautomatic method is used in the fourth step to select the control points, i.e. an automated method for computing the centre of mass of manually identified anatomical structures in neighbouring slices. For the sections which could not be properly corrected by the four steps, a final step is introduced, where control points are manually selected in the reference and sensed images. An algorithm is proposed to examine the spatial distribution of selected control points. Four sets of serial sections of mouse liver cell nuclei, each with approximately 100 sections, are registered by the proposed method and also registered manually for the comparison of registration accuracy. Artificial X-Z and Z-Y sections of registered series were visually compared for the smoothness of the nuclear membrane. To quantify the registration accuracy and the extent of registration, the correlation coefficient (C) and the overlap index (Co) were computed over the registered structure of interest. In addition to the visual comparison and the comparison of C and Co, the registered serial sets were compared by 3D GLCM-based texture features in the Z direction. The results demonstrate that the proposed semiautomatic registration technique achieved accurate results comparable to the manual registration. The proposed registration method relies only on the operator for rough pinpointing of cellular structures. Therefore, it should provide better reproducibility, and allow the user to operate the system faster and in a more relaxed manner than in a manual registration.