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In the realm of medical imaging, precise segmentation of stroke lesions frombrain MRI images stands as a critical challenge with significant implicationsfor patient diagnosis and treatment. Addressing this, our study introduces aninnovative approach using a Fuzzy Information Seeded Region Growing (FISRG)algorithm. Designed to effectively delineate the complex and irregularboundaries of stroke lesions, the FISRG algorithm combines fuzzy logic withSeeded Region Growing (SRG) techniques, aiming to enhance segmentationaccuracy.  The research involved three experiments to optimize the FISRG algorithm'sperformance, each focusing on different parameters to improve the accuracy ofstroke lesion segmentation. The highest Dice score achieved in theseexperiments was 94.2\%, indicating a high degree of similarity between thealgorithm's output and the expert-validated ground truth. Notably, the bestaverage Dice score, amounting to 88.1\%, was recorded in the third experiment,highlighting the efficacy of the algorithm in consistently segmenting strokelesions across various slices.  Our findings reveal the FISRG algorithm's strengths in handling theheterogeneity of stroke lesions. However, challenges remain in areas of abruptlesion topology changes and in distinguishing lesions from similar intensitybrain regions. The results underscore the potential of the FISRG algorithm incontributing significantly to advancements in medical imaging analysis forstroke diagnosis and treatment.

Fuzzy Information Seeded Region Growing for Automated Lesions After Stroke Segmentation in MR Brain Images