Graphical abstractDisplay Omitted HighlightsProposed hybrid methodology is a coarse to fine approach to abdominal organs segmentation.The evolving organ contour is initialized by means of information granule.The image is fuzzified using Gaussian membership function.The granule-adjusted hybrid level set approach yields final segmentation.The approach effectiveness was confirmed based on 20 CT studies and 4 types of organs. Hybrid methodology that combines granular computing with a level set approach for image segmentation is introduced in this paper. The goal of the study is to provide an adjustable semi-automated method for 3D segmentation of abdominal organs in computed tomography studies. Based on initial guidance in terms of models or seed points the information granule is formulated for each anatomical structure under consideration. The granulation leads via spatial volume resampling and granule-driven image intensity fuzzification to the final segmentation stage employing a hybrid level set approach. Depending on the organ intensity and shape the segmentation runs with selected parameters. The algorithm has been evaluated by 20 computed tomography studies and four various structures delineated by an expert. The experimental part presents a comprehensive analysis of subsequent stages of the algorithm. The voxel-wise segmentation sensitivities reached 94.4% for the right kidney, 93.8% for the left kidney, 94.0% for the spleen, and 94.0% for the liver, with the Dice index at 93.7%, 94.2%, 91.0%, and 92.9%, respectively.

Image segmentation is an essential and non trivial task in computer vision and medical image analysis. Computed tomography (CT) is one of the most accessible medical examination techniques to visualize the interior of a patient’s body. Among different computer-aided diagnostic systems, the applications dedicated to kidney segmentation represent a relatively small group. In addition, literature solutions are verified on relatively small databases. The goal of this research is to develop a novel algorithm for fully automated kidney segmentation. This approach is designed for large database analysis including both physiological and pathological cases. This study presents a 3D marker-controlled watershed transform developed and employed for fully automated CT kidney segmentation. The original and the most complex step in the current proposition is an automatic generation of 3D marker images. The final kidney segmentation step is an analysis of the labelled image obtained from marker-controlled watershed transform. It consists of morphological operations and shape analysis. The implementation is conducted in a MATLAB environment, Version 2017a, using i.a. Image Processing Toolbox. 170 clinical CT abdominal studies have been subjected to the analysis. The dataset includes normal as well as various pathological cases (agenesis, renal cysts, tumors, renal cell carcinoma, kidney cirrhosis, partial or radical nephrectomy, hematoma and nephrolithiasis). Manual and semi-automated delineations have been used as a gold standard. Wieclawek Among 67 delineated medical cases, 62 cases are ‘Very good’, whereas only 5 are ‘Good’ according to Cohen’s Kappa interpretation. The segmentation results show that mean values of Sensitivity, Specificity, Dice, Jaccard, Cohen’s Kappa and Accuracy are 90.29, 99.96, 91.68, 85.04, 91.62 and 99.89% respectively. All 170 medical cases (with and without outlines) have been classified by three independent medical experts as ‘Very good’ in 143–148 cases, as ‘Good’ in 15–21 cases and as ‘Moderate’ in 6–8 cases. An automatic kidney segmentation approach for CT studies to compete with commonly known solutions was developed. The algorithm gives promising results, that were confirmed during validation procedure done on a relatively large database, including 170 CTs with both physiological and pathological cases.