Multiscale Morphological Reconstruction for Hair Removal in Dermoscopy Images
Bibliographic record
Abstract
The automatic diagnosis of melanoma is usually affected by the noise that is often included in an image, during the acquisition stage or by superficial factors such as hair. Specifically, hair on the surface of a lesion can cause enough distortion, resulting in an erroneous diagnosis of the region of interest. To solve this issue, several techniques have been presented to detect hair on the surface of a dermoscopy image and substitute a surface approximation for these regions. Nonetheless, the existing methods are prone to false detections or reconstructions that are not uniform, demand high computing resources and modify the textures of important characteristics. Therefore, we proposed a method that detects the hairs by means of a convolution of the image with a kernel belonging to the first derivative of the Gaussian function and replaces the hairs using a multiscale morphological reconstruction. In addition, we integrated a refining stage that contributes to maintaining the quality of the patterns on the lesion. We used 36 dermoscopy images in the evaluation, which included a total of 586 hairs that were automatically detected with the proposed process and validated with their respective manual segmentations. Our results showed sensitivity and specificity performance measurements of 94.14% and 99.89%, respective.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".