An Enhanced CT Liver Segmentation Framework Using Differential Evolution-Optimized Rényi Entropy
Bibliographic record
Abstract
The segmentation of liver images from computed tomography (CT) scans is a pivotal technique that supports various medical applications, including computer-aided diagnostics, disease identification, and the evaluation of hepatic function.In this study, an advanced segmentation method for CT liver images is introduced, leveraging the synergy between Ré nyi entropy and fuzzy c-partition methodologies.The proposed approach commences with the enhancement of input CT images employing an adaptive histogram equalization technique, thereby improving the contrast of hepatic tissues.Subsequently, these images are transformed into the fuzzy domain, wherein the entropies of the hepatic object and the surrounding tissue are meticulously defined.The optimization of the Ré nyi entropy measure is adeptly carried out using the Differential Evolution (DE) algorithm, which establishes precise CT image thresholds for segmentation.The efficacy of the proposed framework is substantiated through extensive experiments, which reveal its superior performance in segmenting liver CT images against complex backgrounds.The results affirm the framework's proficiency, particularly in medical imaging contexts with intricate backdrops, thereby underscoring its potential for enhanced diagnosis and therapeutic planning.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".