Medical & Biological Engineering & Computing https://doi.org/10.1007/s11517-018-1906-0 ORIGINAL ARTICLE Automatic cell segmentation in histopathological images via two-staged superpixel-based algorithms Abdulkadir Albayrak 1,2 · Gokhan Bilgin 1,2 Received: 8 May 2018 / Accepted: 26 September 2018 © International Federation for Medical and Biological Engineering 2018 Abstract The analysis of cell characteristics from high-resolution digital histopathological images is the standard clinical practice for the diagnosis and prognosis of cancer. Yet, it is a rather exhausting process for pathologists to examine the cellular structures manually in this way. Automating this tedious and time-consuming process is an emerging topic of the histopathological image-processing studies in the literature. This paper presents a two-stage segmentation method to obtain cellular structures in high-dimensional histopathological images of renal cell carcinoma. First, the image is segmented to superpixels with simple linear iterative clustering (SLIC) method. Then, the obtained superpixels are clustered by the state- of-the-art clustering-based segmentation algorithms to find similar superpixels that compose the cell nuclei. Furthermore, the comparison of the global clustering-based segmentation methods and local region-based superpixel segmentation algorithms are also compared. The results show that the use of the superpixel segmentation algorithm as a pre-segmentation method improves the performance of the cell segmentation as compared to the simple single clustering-based segmentation algorithm. The true positive ratio (TPR), true negative ratio (TNR), F-measure, precision, and overlap ratio (OR) measures are utilized as segmentation performance evaluation. The computation times of the algorithms are also evaluated and presented in the study. Keywords Histopathological image analysis · Cell segmentation · SLIC · SLIC-DBSCAN · Superpixels 1 Introduction Approximately 14 million people are diagnosed with can- cer by an expert each year, and 8 million of them die of it or related complications. The early diagnosis of cancer is of vital importance in surviving the disease. With technological development, the use of technological devices for diagnosis contributes to the early diagnosis of many diseases and further helps to start necessary treat- ment. Magnetic resonance imaging (MRI) and computed  Gokhan Bilgin gbilgin@yildiz.edu.tr Abdulkadir Albayrak albayrak@yildiz.edu.tr 1 Department of Computer Engineering, Yildiz Technical University (YTU), 34220 Istanbul, Turkey 2 Signal and Image Processing Lab. (SIMPLAB) in YTU, 34220, Istanbul, Turkey tomography (CT) are devices frequently used by experts because they supply reliable information about the internal human structure and functions by various means. Pathology plays an important role in early cancer diagnosis. Following the pathological pre-processes (staining, etc.), pathologists examine the suspected cancerous tissues in the laboratory. These examinations made by pathologists consist of diag- nosis of the disease upon morphological and functional analysis of cellular structures, tissues, and organs. One of the most crucial processes for diagnosis is the determination of cellular structures. The cellular structures of cancerous tissues morphologically differ from the cellular structures of non-cancerous tissues. Analyzing each cellular struc- ture one by one is a difficult and time-consuming process for pathology experts. The aim of this study is to auto- mate this difficult and time-consuming process with the help of imaging equipment and digital image processing techniques. This process is called computer-aided diagno- sis (CAD). The purpose of CAD is to establish secondary decision support systems that contribute to early diagno- sis by analyzing the digitized histopathological images in a computer environment.