An enhanced threshold based technique for white blood cells nuclei automatic segmentation
Why this work is in the frame
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Bibliographic record
Abstract
One of the most important clinical examination tests is the blood test. In a clinical laboratory, counting different blood cells is important. Manual microscopic inspection is time-consuming and requires technical knowledge. Therefore, automatic medical diagnosis systems are required to help physicians to diagnose diseases in a fast and yet efficient way. Cell automatic classification has larger interest especially for clinics and laboratories; the most important step in automatic classification success is segmentation. This paper shows an efficient technique for automatic blood cell nuclei segmentation. This technique is relying on enhancing and filtering the gray scale image contrast. False objects are removed utilizing minimum segment size. 365 blood images were used to examine this segmentation technique. Quantitative analysis of the proposed segmentation technique on the blood image set gives 80.6% accuracy. In comparison to other techniques the proposed segmentation technique performance was found to be superior. The five normal white blood cells types were used for evaluation to compare isolated performance. Eosinophil was found to have the lowest segmentation accuracy which is 71.0% and Monocyte was the highest one with 85.9%. The blood images dataset and the source code are published on MATLAB file exchange website for comparison and re-production.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.001 | 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 it