Spinal Cord Based Kidney Segmentation Using Connected Component Labeling and K-Means Clustering Algorithm

Abstract

Abdominal computed tomography (CT) data are often used in the diagnosis and treatment of patients. Segmentation of viscera on abdominal imaging facilitates diagnosis and focus upon the areas of interest. Kidney segmentation by abdominal imaging is complicated by the proximity of various organs and the similarities between abdominal tissues. Here we propose two fully automated approaches to kidney segmentation and discuss their performance. A fully automated approach was preferred to accelerate the decision-making process of the physician to eliminate the disadvantage of manual and semi-automatic segmentations. Each of the proposed methods essentially consist of three stages. Since the spine was used as reference in the study, the images were first treated to define the coordinates of the spine. Second, kidney fields were obtained using the Connected Component Labeling (CCL) and the K-means clustering algorithms. Last, the kidneys were segmented by applying different filters according to the method. A manual segmentation was then performed by specialist physicians. The performance of the tested algorithms was made by comparison to the manual segmentation results, using the Dice Similarity Coefficient, the Figures of Merit and Jaccard Similarity Index. Based on our analyses, acceptable success rates were achieved by the proposed methodologies. These automated systems are expected to be helpful during clinical diagnosis, medical training and future studies on kidney cancer diagnosis.