ArticleMedical & biological engineering & computing2018
Delineation of the ischemic stroke lesion based on watershed and relative fuzzy connectedness in brain MRI.
Article in Medical & biological engineering & computing, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Systematic review of novel technology-based interventions for ischemic stroke.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2021Pooled it
- The value and limitations of the Oxford Community Stroke Project classification in the hyperacute phase of cerebral infarction.Frontiers in neurology · 2025Review
- Diagnostic Models for Differentiating COVID-19-Related Acute Ischemic Stroke Using Machine Learning Methods.Diagnostics (Basel, Switzerland) · 2024Article
- The role of input imaging combination and ADC threshold on segmentation of acute ischemic stroke lesion using U-Net.European radiology · 2023Article
- Application of Machine Learning Techniques for Characterization of Ischemic Stroke with MRI Images: A Review.Diagnostics (Basel, Switzerland) · 2022Review
- A customized acutance metric for quality control applications in MRI.Medical & biological engineering & computing · 2022Article
- Pseudo-Label-Assisted Self-Organizing Maps for Brain Tissue Segmentation in Magnetic Resonance Imaging.Journal of digital imaging · 2022Article
- Segmentation of MRI brain scans using spatial constraints and 3D features.Medical & biological engineering & computing · 2020Article
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Authors and funding
3 authors.
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No grant is acknowledged in the PubMed record.
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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