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Contrast enhanced medical MRI evaluation using Tsallis entropy and region growing segmentation

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Abstract

In medical domain, diseases in critical internal organs are generally inspected using invasive/non-invasive imaging techniques. Magnetic resonance imaging (MRI) is one of the commonly considered imaging approaches to confirm the abnormality in various internal organs. After recording the MRI, an appropriate image processing exercise is to be implemented to investigate and infer the severity of the disease and its location. This paper proposes a semi-automated tool to investigate the medical MRI captured with contrast improved T1 modality (T1C). This technique considers the integration of Bat algorithm (BA) and Tsallis based thresholding along with region growing (RG) segmentation. Proposed approach is tested on RGB/gray scale images of brain and breast MRI recorded along with a contrast agent. After mining the infected region, its texture features are extracted with Haralick function to assess the surface details of abnormal section. Performance of RG is confirmed against other segmentation methods, such as level set (LS), principal component analysis (PCA) and watershed. The clinical significance of the proposed technique is also validated using the brain images of BRATS recorded using T1C modality. The experiment outcome confirms that, the implemented procedure provides better values of Jaccard (87.41%), Dice (90.36%), sensitivity (98.27%), specificity (97.72%), accuracy (97.53%) and precision (95.85%) for the considered BRATS brain MRI.

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Acknowledgements

The authors of this paper would like to acknowledge M/S. Proscans Diagnostics Pvt. Ltd., a leading scan centre in Chennai for providing the clinical brain MRI for experimental investigation.

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Authors and Affiliations

  1. Department of Electronics and Instrumentation Engineering, St. Joseph’s College of Engineering, OMR, Chennai, Tamilnadu, 600119, India

    N. Sri Madhava Raja & V. Rajinikanth

  2. Department of Electronics and Communication Engineering, Sahyadri College of Engineering and Management, Mangalore, Karnataka, 575007, India

    S. L. Fernandes

  3. Department of Information Technology, Techno India College of Technology, Kolkata, West Bengal, 700156, India

    Nilanjan Dey

  4. School of Computer Engineering, Kalinga Institute of Industrial Technology (Deemed to be University), Bhubaneswar, Odisha, 751024, India

    Suresh Chandra Satapathy

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  1. N. Sri Madhava Raja
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  5. V. Rajinikanth
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Correspondence to V. Rajinikanth.

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Raja, N.S.M., Fernandes, S.L., Dey, N. et al. Contrast enhanced medical MRI evaluation using Tsallis entropy and region growing segmentation. J Ambient Intell Human Comput 15, 961–972 (2024). https://doi.org/10.1007/s12652-018-0854-8

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