TY - JOUR N2 - The liver is a vital organ of the human body and hepatic cancer is one of the major causes of cancer deaths. Early and rapid diagnosis can reduce the mortality rate. It can be achieved through computerized cancer diagnosis and surgery planning systems. Segmentation plays a major role in these systems. This work evaluated the efficacy of the SegNet model in liver and particle swarm optimization-based clustering technique in liver lesion segmentation. Over 2400 CT images were used for training the deep learning network and ten CT datasets for validating the algorithm. The segmentation results were satisfactory. The values for Dice Coefficient and volumetric overlap error achieved were 0.940 ± 0.022 and 0.112 ± 0.038, respectively for liver and the results for lesion delineation were 0.4629 ± 0.287 and 0.6986 ± 0.203, respectively. The proposed method is effective for liver segmentation. However, lesion segmentation needs to be further improved for better accuracy. L1 - http://journals.pan.pl/Content/124275/PDF/23-3511-12099-1-PB.pdf L2 - http://journals.pan.pl/Content/124275 PY - 2022 IS - No 3 EP - 640 DO - 10.24425/ijet.2022.141283 KW - Liver lesion segmentation KW - computed tomography KW - semantic segmentation KW - SegNet KW - Particle swarm optimization-based clustering KW - Hounsfield Unit A1 - Nayantara, P Vaidehi A1 - Kamath, Surekha A1 - KN, Manjunath A1 - Kadavigere, Rajagopal PB - Polish Academy of Sciences Committee of Electronics and Telecommunications VL - vol. 68 DA - 2022.09.06 T1 - Semantic segmentation and PSO based method for segmenting liver and lesion from CT images SP - 635 UR - http://journals.pan.pl/dlibra/publication/edition/124275 T2 - International Journal of Electronics and Telecommunications ER -