Fuzzy Information Seeded Region Growing for Automated Lesions After Stroke Segmentation in MR Brain Images
This addresses the challenge of precise lesion segmentation for stroke diagnosis and treatment in medical imaging, but it is incremental as it builds on existing seeded region growing techniques.
The study tackled the problem of segmenting stroke lesions from brain MRI images by introducing a Fuzzy Information Seeded Region Growing (FISRG) algorithm, achieving a highest Dice score of 94.2% and an average of 88.1% in experiments.
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.