Abstract
An automatic method for segmenting the liver from the portal venous phase of abdominal CT images using the K-Means clustering method is described in this paper. We have incorporated an interactive technique for correcting the errors in the liver segmentation results using power law transformation. The proposed method was validated on abdominal CT volumes of fifteen patients obtained from Kasturba Medical College, Manipal. The average values of the various standard evaluation metrics obtained are as follows: Dice coefficient = 0.9361, Jaccard index = 0.8805, volumetric overlap error = 0.1195, absolute volume difference = 4.048%, average symmetric surface distance = 1.7282 mm and maximum symmetric surface distance = 38.039 mm. The quantitative and qualitative results obtained in our preliminary work show that the K-Means clustering technique along with power law transformation is effective in producing good liver segmentation outputs. As future work, we will attempt to automate the power law transformation technique.
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Acknowledgments
The work is supported by KStePS, DST, Government of Karnataka, India. The authors are grateful to Manipal Institute of Technology, MAHE, Manipal for providing the facilities to carry out the research and Kasturba Medical College, Manipal, for providing the patient data.
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Nayantara, P.V., Kamath, S., Manjunath, K.N., Rajagopal, K.V. (2021). A Liver Segmentation Algorithm with Interactive Error Correction for Abdominal CT Images: A Preliminary Study. In: Krishnamurthy, V., Jaganathan, S., Rajaram, K., Shunmuganathan, S. (eds) Computational Intelligence in Data Science. ICCIDS 2021. IFIP Advances in Information and Communication Technology, vol 611. Springer, Cham. https://doi.org/10.1007/978-3-030-92600-7_13
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