# attachment[].name
Image from 'Exploring Image Analysis in R' by Brauckhoff et al. The image contains two parts: Figure 12 and Figure 13.  Figure 12 displays two fluorescence microscopy images of cells with stained nuclei. In image A, the nuclei are blue, and DNA double-strand breaks (H2AX foci) are highlighted in red. Image B shows a similar view but with different color highlighting. Figure 13 is a bar graph showing the frequency distribution of gamma H2AX foci counts per cell. The x-axis represents foci count (0 to 20), and the y-axis represents frequency.

# content
📢 @devTLB@mastodon.social and I’ve just published a review article titled **“Exploring Image Analysis in R: Applications and Advancements”** in *The R Journal* (Vol. 17, Issue 3).

🔍 The paper surveys #rstats image‑analysis packages, from the early work of *adimpro* and *EBImage* to the newer packages like *biopixR*. It shows that the majority of these packages are still active, with two‑thirds updated within the last 1.5 years. A robust and vibrant ecosystem for bioimage informatics.

📊 Key takeaways:

- Comprehensive overview of tools for multiplex imaging, cell tracking, and general-purpose analysis.

- Emphasis on end‑to‑end pipelines that extract meaningful biological insights from images.

- Real‑world examples demonstrating how R can address current scientific challenges in image analysis.

🔗 You can read the full article here: doi.org/10.32614/RJ-2025-030

I’m grateful for the support of my co‑authors Tim Brauckhoff, Julius Rublack. Feel free to share your thoughts or ask questions about applying these methods in your own work!

# source.content
📢 [@devTLB@mastodon.social](https://mastodon.social/users/devTLB) and I’ve just published a review article titled \*\*“Exploring Image Analysis in R: Applications and Advancements”\*\* in \*The R Journal\* (Vol. 17, Issue 3).

🔍 The paper surveys [#rstats](https://sandbox.openscience.network/hashtag/rstats) image‑analysis packages, from the early work of \*adimpro\* and \*EBImage\* to the newer packages like \*biopixR\*. It shows that the majority of these packages are still active, with two‑thirds updated within the last 1.5 years. A robust and vibrant ecosystem for bioimage informatics.

<br/>

📊 Key takeaways:

<br/>

\- Comprehensive overview of tools for multiplex imaging, cell tracking, and general-purpose analysis.

\- Emphasis on end‑to‑end pipelines that extract meaningful biological insights from images.

\- Real‑world examples demonstrating how R can address current scientific challenges in image analysis.

<br/>

🔗 You can read the full article here: [doi.org/10.32614/RJ-2025-030](https://doi.org/10.32614/RJ-2025-030)

I’m grateful for the support of my co‑authors Tim Brauckhoff, Julius Rublack. Feel free to share your thoughts or ask questions about applying these methods in your own work!