Comparative Study of Rough Set-Based FCM and K-Means Clustering for Tumor Segmentation from Brain MRI Images
Bibliographic record
Abstract
Segmentation of tumors from brain Magnetic Resonance Imaging (MRI) imagery is of utmost importance, particularly given their diverse morphologies and contrasts.In this paper, two novel methods of tumor segmentation are proposed, both employing Rough Set Theory in conjunction with clustering techniques: Fuzzy C-Means (FCM) and K-Means.In the first methodology, the cluster centers derived from FCM are incorporated into a Rough Set model to facilitate segmentation.Conversely, the second methodology utilizes cluster centers from K-Means clustering within the Rough Set framework for the same purpose.These techniques have been implemented on two publicly available brain MRI datasets.For preprocessing, an initial thresholding step is executed, followed by extraction of the foreground region via a binary mask.The Rough Set-based FCM is then applied to the binary image, generating cluster centers that are subsequently utilized by the Rough Set to accurately segment the tumor region.A similar approach is employed in the Rough Setbased K-Means clustering methodology.Experimental results indicate that the hybridization of Rough Set Theory with K-Means outperforms standard FCM, K-Means clustering, and FCM clustering in terms of accuracy, precision, recall, f-measure, and computational time.The average accuracy was found to be at a minimum for FCM (53.95%) and at a maximum for K-Means-based Rough Set Theory (95.77%).Moreover, the average clustering time was shortest for K-Means clustering (6.256s), and longest for FCM-based Rough Set Theory (43.27s).This study thus presents significant advancements in the field of tumor segmentation from brain MRI images, with potential implications for improved diagnostic accuracy and patient outcomes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".