Fuzzy Information Seeded Region Growing for Automated Lesions After
  Stroke Segmentation in MR Brain Images
Mario Pascual González
In the realm of medical imaging, precise segmentation of stroke lesions from
brain MRI images stands as a critical challenge with significant implications
for patient diagnosis and treatment. Addressing this, our study introduces an
innovative approach using a Fuzzy Information Seeded Region Growing (FISRG)
algorithm. Designed to effectively delineate the complex and irregular
boundaries of stroke lesions, the FISRG algorithm combines fuzzy logic with
Seeded Region Growing (SRG) techniques, aiming to enhance segmentation
accuracy.
  The research involved three experiments to optimize the FISRG algorithm's
performance, each focusing on different parameters to improve the accuracy of
stroke lesion segmentation. The highest Dice score achieved in these
experiments was 94.2\%, indicating a high degree of similarity between the
algorithm's output and the expert-validated ground truth. Notably, the best
average Dice score, amounting to 88.1\%, was recorded in the third experiment,
highlighting the efficacy of the algorithm in consistently segmenting stroke
lesions across various slices.
  Our findings reveal the FISRG algorithm's strengths in handling the
heterogeneity of stroke lesions. However, challenges remain in areas of abrupt
lesion topology changes and in distinguishing lesions from similar intensity
brain regions. The results underscore the potential of the FISRG algorithm in
contributing significantly to advancements in medical imaging analysis for
stroke diagnosis and treatment.