Counting of RBCs and WBCs in noisy normal blood smear microscopic images
Why this work is in the frame
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Bibliographic record
Abstract
This work focuses on the segmentation and counting of peripheral blood smear particles which plays a vital role in \nmedical diagnosis. Our approach profits from some powerful processing techniques. Firstly, the method used for \ndenoising a blood smear image is based on the Bivariate wavelet. Secondly, image edge preservation uses the Kuwahara \nfilter. Thirdly, a new binarization technique is introduced by merging the Otsu and Niblack methods. We have also \nproposed an efficient step-by-step procedure to determine solid binary objects by merging modified binary, edged \nimages and modified Chan-Vese active contours. The separation of White Blood Cells (WBCs) from Red Blood Cells \n(RBCs) into two sub-images based on the RBC (blood’s dominant particle) size estimation is a critical step. Using \nGranulometry, we get an approximation of the RBC size. The proposed separation algorithm is an iterative mechanism \nwhich is based on morphological theory, saturation amount and RBC size. A primary aim of this work is to introduce an \naccurate mechanism for counting blood smear particles. This is accomplished by using the Immersion Watershed \nalgorithm which counts red and white blood cells separately. To evaluate the capability of the proposed framework,experiments were conducted on normal blood smear images. This framework was compared to other published \napproaches and found to have lower complexity and better performance in its constituent steps; hence, it has a better \noverall performance.
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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 | 0.002 |
| 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