# Tissue Background Segmentation

> Use when you have loaded a laser ablation ICP-MS image into pew² and need to distinguish tissue signal from instrument background or non-ablated substrate.

- Skill: `holobiomicslab/tissue-background-segmentation` (Agent Skill)
- Install (CLI): `npx skillmds@latest add holobiomicslab/tissue-background-segmentation`
- Raw SKILL.md: https://api.skillmd.com/api/skills/holobiomicslab/tissue-background-segmentation/raw
- Safety review: PASS (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- License: CC-BY-4.0
- Author: HolobiomicsLab (https://skillmd.com/u/holobiomicslab)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/holobiomicslab/tissue-background-segmentation

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# Threshold-Based Tissue–Background Segmentation in LA-ICP-MS Images

> **License: restricted** — no clear open-source license detected for the underlying tool; verify licensing before commercial use or redistribution. <!-- asb-license-banner -->
## Summary

Separates tissue pixels from background in laser ablation ICP-MS images using threshold-based methods (e.g., Otsu's method) applied through pew²'s Selection Dialog. This is essential for isolating regions of analytical interest before quantification or spatial analysis.

## When to use

You have loaded a laser ablation ICP-MS image into pew² and need to distinguish tissue signal from instrument background or non-ablated substrate. Use this skill when the tissue and background occupy distinct intensity ranges and you want to create a binary mask that restricts downstream filtering, calibration, or export to tissue-only regions.

## When NOT to use

- Input image already has a pre-existing, validated tissue mask — use the mask filter directly instead.
- Tissue and background have highly overlapping intensity distributions with no clear threshold — consider k-means clustering or manual region-of-interest selection.
- You need to preserve intensity values within the tissue region — use mask filter after segmentation, not segment alone.

## Inputs

- LA-ICP-MS image (line-by-line, spot-wise, or ablation-time-aligned format supported by pew²)
- Intensity data array (single element or mass channel)

## Outputs

- Binary mask image (pixels marked as tissue or background/NaN)
- Selection overlay (visual confirmation on original image)

## How to apply

Open the Selection Dialog via right-click context menu on the loaded LA-ICP-MS image in pew². Choose a thresholding method from the Method combo-box: Otsu's method (otsu filter) automatically computes an optimal threshold to minimize within-class variance, or manually set a threshold value. Set the Comparison operator (e.g., '>' to select pixels above the threshold, or '<' below) to define your segmentation criterion. Optionally restrict thresholding to an existing selection by checking 'Limit thresholding to selected value'. Apply the threshold to generate a binary mask. Verify segmentation quality by visualizing the resulting selection overlay on the image; adjust threshold parameters or operator if tissue/background separation is incomplete or includes spurious regions.

## Related tools

- **pew²** (GUI for importing, visualizing, and applying threshold-based segmentation to LA-ICP-MS images via the Selection Dialog) — https://github.com/djdt/pewpew
- **pewlib** (Python library providing segment, otsu, and mask filter operations underlying pew²'s segmentation workflow) — https://github.com/djdt/pewlib

## Evaluation signals

- Binary mask contains only 0/1 or tissue/NaN values with no intermediate intensities.
- Tissue pixels correspond to visually identifiable anatomical features in the original image overlay.
- Selection boundary does not include spurious noise clusters or exclude continuous tissue regions.
- Threshold value falls within the observed intensity range; operator direction (>, <, >=, <=) is consistent with tissue/background separation goal.
- Mask can be successfully applied downstream (via 'mask' filter) to isolate tissue-only data for calibration or export without artifact propagation.

## Limitations

- Otsu's method assumes a bimodal intensity distribution; unimodal or multimodal data may produce suboptimal thresholds.
- Fixed threshold does not adapt to spatial heterogeneity in ablation efficiency or matrix effects across the sample.
- Thresholding is blind to morphological context; small isolated pixels or thin filaments may be misclassified.
- No built-in edge-refinement or morphological post-processing (e.g., erosion/dilation) to smooth mask boundaries.
- Relies on manual threshold tuning if automatic methods (Otsu) do not yield acceptable results.

## Evidence

- [other] The pew² application implements segmentation through filter operations including Otsu's method and threshold-based masking: the 'segment' filter creates a mask image from thresholds, the 'otsu' filter applies Otsu's method to an image, and the 'mask' filter selects regions from an image using a binary mask.: "the 'segment' filter creates a mask image from thresholds, the 'otsu' filter applies Otsu's method to an image, and the 'mask' filter selects regions from an image using a binary mask"
- [other] Open the Selection Dialog via right-click context menu on the image. Select a thresholding method (e.g., Otsu's method) from the Method combo-box and set the Comparison operator (e.g., '>') to define the segmentation criterion.: "Open the Selection Dialog via right-click context menu on the image. Select a thresholding method (e.g., Otsu's method) from the Method combo-box and set the Comparison operator"
- [other] Optionally check 'Limit thresholding to selected value' to restrict the thresholding operation to a pre-existing selection. Apply the threshold to generate a binary mask separating tissue pixels from background.: "Apply the threshold to generate a binary mask separating tissue pixels from background"
- [readme] Pew² is a GUI for importing and processing line-by-line, spot-wise and ablation-time-aligned LA-ICP-MS data: "GUI for importing and processing line-by-line, spot-wise and ablation-time-aligned LA-ICP-MS data"
- [methods] segment |image, threshold |creates a mask image of `thesholds`: "segment |image, threshold |creates a mask image of `thesholds`"