Abstract
Image segmentation plays a vital role in medical image processing for the delineation of anatomical organs and analysis of anomalies. The evaluation of segmentation algorithms is vital to select the appropriate algorithm and parameters for optimum performance. In this paper, we are describing various metrics for evaluating the quality of segmentation algorithms with respect to ground truth images. The analysis of metrics has been carried out on real-time data sets of abdomen and retina. The variants of active contour algorithms are employed for the abdomen CT images, Kirsch and Wavelet algorithm were used for the retinal fundus images. This paper presents performance evaluation parameters that can be used to analyze efficiency of segmentation algorithms.
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Acknowledgements
The authors would like to acknowledge the support provided by DST under IDP scheme (No: IDP/MED/03/2015). We thank Dr. Sebastian Varghese (Consultant Radiologist, Metro Scans and Laboratory, Trivandrum) for providing the medical CT/MR images and supporting us in the preparation of manuscript.
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Kumar, S.N., Lenin Fred, A., Ajay Kumar, H., Sebastin Varghese, P. (2018). Performance Metric Evaluation of Segmentation Algorithms for Gold Standard Medical Images. In: Sa, P., Bakshi, S., Hatzilygeroudis, I., Sahoo, M. (eds) Recent Findings in Intelligent Computing Techniques . Advances in Intelligent Systems and Computing, vol 709. Springer, Singapore. https://doi.org/10.1007/978-981-10-8633-5_45
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