Fuzzy Information Seeded Region Growing for Automated Lesions After Stroke Segmentation in MR Brain Images
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.
Code (1)
Tasks
Lesion SegmentationSegmentationSimilar Papers 제목 키워드 기반
Weakly-Supervised Semantic Segmentation Network With Deep Seeded Region Growing
This paper studies the problem of learning image semantic segmentation networks only using image-level labels as supervision, which is important since it can significantly reduce human annotation efforts. Recent state-of…
Image SegmentationSegmentationSemantic SegmentationWeakly supervised Semantic Segmentation+1An Automatic Seeded Region Growing for 2D Biomedical Image Segmentation
In this paper, an automatic seeded region growing algorithm is proposed for cellular image segmentation. First, the regions of interest (ROIs) extracted from the preprocessed image. Second, the initial seeds are automati…
Image SegmentationSegmentationSemantic SegmentationA combined Approach Based on Fuzzy Classification and Contextual Region Growing to Image Segmentation
We present in this paper an image segmentation approach that combines a fuzzy semantic region classification and a context based region-growing. Input image is first over-segmented. Then, prior domain knowledge is used t…
General ClassificationImage SegmentationSegmentationSemantic SegmentationAutomatic Color Image Segmentation Using a Square Elemental Region-Based Seeded Region Growing and Merging Method
This paper presents an efficient automatic color image segmentation method using a seeded region growing and merging method based on square elemental regions. Our segmentation method consists of the three steps: generati…
Image SegmentationSegmentationSemantic SegmentationAdversarial Seeded Sequence Growing for Weakly-Supervised Temporal Action Localization
Temporal action localization is an important yet challenging research topic due to its various applications. Since the frame-level or segment-level annotations of untrimmed videos require amounts of labor expenditure, st…
Action DetectionAction LocalizationTemporal Action LocalizationWeakly-supervised Temporal Action Localization