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Delineation of the ischemic stroke lesion based on watershed and relative fuzzy connectedness in brain MRI

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Abstract

Precise segmentation of stroke lesions from brain magnetic resonance (MR) images poses a challenging task in automated diagnosis. In this paper, we proposed a new method called watershed-based lesion segmentation algorithm (WLSA), which is a novel intensity-based segmentation technique used to delineate infarct lesion in diffusion-weighted imaging (DWI) MR images of the brain. The algorithm was tested on a series of 142 real-time images collected from different stroke patients reported at IMS and SUM Hospital. One MRI slice having largest area of infract lesion is selected from each patient from multiple slices. The main objective is to combine the strength of guided filter and watershed transform through relative fuzzy connectedness (RFC) to detect lesion boundaries appropriately. The extracted informative statistical and geometrical features are used to classify the types of stroke lesions according to the Oxfordshire Community Stroke Project (OCSP) classification. The experimental results demonstrated the effectiveness of the proposed process with high accuracy in delineating lesions. A classification with a dice similarity index (DSI) of 96% with computational time of 0.06 s in random forest (RF) and an accuracy of 85% with computational time of 0.84 s has been obtained by multilayer perceptron (MLP) neural network classifier in tenfold cross-validation process. Better detection accuracy is achieved in RF classifier in classifying stroke lesions.

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Acknowledgements

We express our sincere gratitude to the technicians of IMS & SUM Hospital, for providing the MRI images of stroke patients.

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, ITER, SOA University, Bhubaneswar, Odisha, India

    Asit Subudhi

  2. Department of Neurology, All India Institutes of Medical Sciences, Bhubaneswar, Odisha, India

    Subhranshu Jena

  3. Department of Electronics Engineering, DY Patil Ramrao Adik Institute of Technology, Navi Mumbai, India

    Sukanta Sabut

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  1. Asit Subudhi
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  2. Subhranshu Jena
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  3. Sukanta Sabut
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Correspondence to Sukanta Sabut.

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Subudhi, A., Jena, S. & Sabut, S. Delineation of the ischemic stroke lesion based on watershed and relative fuzzy connectedness in brain MRI. Med Biol Eng Comput 56, 795–807 (2018). https://doi.org/10.1007/s11517-017-1726-7

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