Abstract

Accurate liver segmentation from CT images is essential for diagnosis, treatment planning, and surgical interventions in hepatic diseases. Manual segmentation is time-consuming and subject to inter-observer variability, necessitating efficient automated solutions. This study proposes a novel liver segmentation approach based on the Jaccard with interpolation scaled tuna swarm-based fuzzy c-means (JISTS-FCM) clustering algorithm. The methodology comprises four primary stages: pre-processing, feature extraction, clustering, and bounding box generation. Pre-processing enhances CT images using Z-score normalization, contrast enhancement through covarianced contrast limited adaptive histogram equalization (CCLAHE), and noise reduction via the edge preserved median filter (EPMF). Feature extraction is performed using the local tetra pattern (LTP) and adaptive edge detectors. For segmentation, the proposed JISTS-FCM algorithm integrates the tuna swarm optimization (TSO) to improve cluster initialization, replaces Euclidean distance with the Jaccard similarity measure, and incorporates interpolation scaling to enhance convergence and segmentation precision. The random walker method is then used to construct accurate bounding boxes around the segmented liver regions. The method is validated on the 3D-IRCADb dataset, achieving superior performance with a dice similarity coefficient (DSC) of 97.55%, outperforming baseline methods including traditional FCM and cuckoo search algorithm (CSA)-FCM. Additional metrics such as Jaccard similarity coefficient (JSC), volumetric overlap error (VOE), average symmetric surface distance (ASSD), relative volume difference (RVD), and root mean square symmetric surface distance (RMSSD) further confirm the accuracy and robustness of the proposed method. The results demonstrate that JISTS-FCM effectively addresses over-segmentation, irregular boundary delineation, and computational inefficiencies, providing a reliable and automated liver segmentation framework for clinical applications.

Keywords

Liver segmentation, Fuzzy c-means clustering, Tuna swarm optimization, Jaccard similarity, Medical image processing, Computed tomography.

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