Semantic segmentation and PSO based method for segmenting liver and lesion from CT images

P Vaidehi Nayantara, Surekha Kamath, Manjunath KN, Rajgopal Kadavigere


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. The method was evaluated on portal
venous phase CT images obtained from ten patients at Kasturba
Hospital, Manipal. 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.

Full Text:



J. Ozougwu, “Physiology of the liver,” emphInternational Journal of Research in Pharmacy and Biosciences, vol. 4, pp. 13–24, Jan. 2017.

A. Adcock, D. Rubin, and G. Carlsson, “Classification of hepatic lesions

using the matching metric,” Comput. Vis. Image Underst., vol. 121, pp. 36–42, 2014,

S. G. Mougiakakou, I. K. Valavanis, A. Nikita, and K. S. Nikita, Differential diagnosis of CT focal liver lesions using texture features, feature selection and ensemble driven classifiers,” Artif. Intell. Med., vol. 41, no. 1, pp. 25–37, 2007,


L. Balagourouchetty, J. K. Pragatheeswaran, B. Pottakkat, and R. Govindarajalou, “Enhancement approach for liver lesion diagnosis using unenhanced CT images,” IET Comput. Vis., vol. 12, no. 8, pp. 1078–1087,


A. Nayak et al., “Computer-aided diagnosis of cirrhosis and hepatocellular carcinoma using multi-phase abdomen CT,” Int. J. Comput. Assist. Radiol. Surg., vol. 14, no. 8, pp. 1341–1352, 2019,

L. Meng, Y. Tian, and S. Bu, “Liver tumor segmentation based on 3D

convolutional neural network with dual scale,” J. Appl. Clin. Med. Phys.,

vol. 21, no. 1, pp. 144–157, 2020,

S. Rafiei et al., “Liver segmentation in abdominal CT images by adaptive

D region growing,” arXiv Prepr. arXiv1802.07794, 2018.

X. Yang et al., “A hybrid semi-automatic method for liver segmentation based on level-set methods using multiple seed points,” Comput. Methods Programs Biomed., vol. 113, no. 1, pp. 69–79, 2014,

G. I. Sayed, A. E. Hassanien, and G. Schaefer, “An Automated

Computer-aided Diagnosis System for Abdominal CT Liver Images,” Procedia Comput. Sci., vol. 90, no. July, pp. 68–73, 2016,

L. Xu, Y. Zhu, Y. Zhang, and H. Yang, “Liver segmentation based on

region growing and level set active contour model with new signed

pressure force function,” Optik (Stuttg)., vol. 202, no. July 2019, 2020,

P. Campadelli, E. Casiraghi, and A. Esposito, “Liver segmentation from computed tomography scans: A survey and a new algorithm,” Artif. Intell. Med., vol. 45, no. 2–3, pp. 185–196, 2009,

J. Li et al., “A fully automatic computer-aided diagnosis system for hepatocellular carcinoma using convolutional neural networks,” Biocybern. Biomed. Eng., vol. 40, no. 1, pp. 238–248, 2020,

S. LI, G. K. F. TSO, and K. HE, “Bottleneck feature supervised U-Net

for pixel-wise liver and tumor segmentation,” Expert Syst. Appl., vol.

, p. 113131, 2020,

V. Badrinarayanan, A. Kendall, and R. Cipolla, “SegNet: A Deep

Convolutional Encoder-Decoder Architecture for Image Segmentation,”

IEEE Trans. Pattern Anal. Mach. Intell., vol. 39, no. 12, pp. 2481–2495,


K. Simonyan and A. Zisserman, “Very deep convolutional networks for

large-scale image recognition,” 3rd Int. Conf. Learn. Represent. ICLR

- Conf. Track Proc., pp. 1–14, 2015.

D. W. Van Der Merwe and A. P. Engelbrecht, “Data clustering using particle swarm optimization,” in 2003 Congress on Evolutionary

Computation, CEC 2003 - Proceedings, 2003, vol. 1, pp. 215–220,

L. Soler et al., “3D image reconstruction for comparison of algorithm

database: a patient-specific anatomical and medical image database.

IRCAD, Strasbourg,” France, Tech. Rep, 2010.

P. A. Yushkevich, Y. Gao, and G. Gerig, “ITK-SNAP: An interactive

tool for semi-automatic segmentation of multi-modality biomedical

images,” in emph2016 38th Annual International Conference of the

IEEE Engineering in Medicine and Biology Society (EMBC), 2016,

pp. 3342–3345.

P. A. Yushkevich et al., “User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency

and reliability,” Neuroimage, vol. 31, no. 3, pp. 1116–1128, 2006,

M. Moghbel, S. Mashohor, R. Mahmud, and M. I. Bin Saripan, “Review

of liver segmentation and computer assisted detection/diagnosis methods

in computed tomography,” Artif. Intell. Rev., vol. 50, no. 4, pp. 497–537,


A. Danilov and A. Yurova, “Automated segmentation of abdominal

organs from contrast-enhanced computed tomography using analysis of

texture features,” Int. j. numer. method. biomed. eng., vol. 36, no. 4, pp.

–14, 2020,

J. Peng, F. Dong, Y. Chen, and D. Kong, “A region-appearance-based

adaptive variational model for 3D liver segmentation,” Med. Phys., vol.

, no. 4, p. 43502, Apr. 2014,

Y. Chen, Z. Wang, J. Hu, W. Zhao, and Q. Wu, “The domain knowledge based graph-cut model for liver CT segmentation,”

Biomed. Signal Process. Control, vol. 7, no. 6, pp. 591–598, 2012,


  • There are currently no refbacks.

International Journal of Electronics and Telecommunications
is a periodical of Electronics and Telecommunications Committee
of Polish Academy of Sciences

eISSN: 2300-1933