Essentially you want to firstly select all the tissue area (both red/orange and pink) using thresholding and create an ROI to measure area. Then you want to use thresholding to just select the pink area and create an ROI to measure area. If you want to make it even more accurate you could exclude around vessels.

Here's a guide: ImageJ: Thresholding — Introduction to Bioimage Analysis (bioimagebook.github.io)

https://bioimagebook.github.io/chapters/2-processing/3-thresholding/imagej.html

Dr Joseph, thank you for the answer. after many trials, first I convert all area in binary (black) - select - measure total area in the check box. and than open the same image and choose only the red area and convert to binary - measure. The result then copied to Ms. Excel and made the percentage. It was hard in tresholding part.

The deconvolution of original image could help a lot to make process of analysis smooth.

Please check my responses on same problem for Picro Sirius Red staining:

https://www.researchgate.net/post/RGBtoLAB_error_what_is_missing

https://www.researchgate.net/post/Picrosirius_Red_staining_on_frozen_heart_sections_possible

Other matherials:

Article A convenient method for quantifying collagen fibers in ather...

Article Elastic Fibres in the subcutaneous tissue: Is there a differ...

https://imagej.nih.gov/ij/docs/examples/stained-sections/index.html

So, you could create you own deconvolution vector to facilitate analysis.

The principles of analysis and process of new deconvolution vector creation described here https://blog.bham.ac.uk/intellimic/g-landini-software/colour-deconvolution-2/

I took settings for SiriusRed from this page https://github.com/MontpellierRessourcesImagerie/imagej_macros_and_scripts/wiki/MRI_Fibrosis_Tool

Have a nice analysis

Even simple color deconvolution RGB is giving good separation on you picture! so FIJI menu: Image/color/color deconvolution -> select RGB

Use 1st color (red on resulting picture) as a mask for full area and 3rd color (blue on resulting picture) as a areas stained for collagen.

All other comments from previous commenters are in force (exluding repivascular area and vessel openings, cracks and other not tissue structures). Thresholding alone is not easy to use from point of reproducibility but after color deconvolution threshold works excellent (be sure not to include background and use same settings for all chanels and images). If you have not uniform brightfield it should be corrected before automatic analysis.

For the last step to measure area you could convert mask created with threshold to selection by using menu: Edit/selection/create selection

After that you coul save it by ROI manager (Ctrl+T) or just measure (M) and use obtained numbers for calculation. So threshold 0-214 msk gave 916487pxl/2228963 pxl*100% = 41.12 % afected area on provided image.

If you have more specific question do not hesitate to contact me.

dear Dr. Daniil R. Petrenyov , It is easy to apply, thank you for your help. in term of tresholding, i know that it can be subjective. should we have more than 1 person to calculate to make it more objective?

You are welcome Khoirur,

Tottaly agree with you. It is always better to keep in mind reproducibility of staining results. F.e. we included one sample with positive and negative staning as a internal standard for each staining batch to confirm staining efficiency. To establish new method it is extremelly important to perform correlation analysis of result obtained by both aproaches (manual tracing and deconvolution with following automatic estimation of scar area by treshold). >should we have more than 1 observer to measure it?can it make more objective if we have more than 1 observer? Yes if you have a chance to have more than one observer it is great to compare results on small number of samples. From my point of view measurement of scar area by deconvolution + threshold is les subjective than estimation by observer (quality control of staining, unifomf field of view and removing of perivascular area prior analysis are obligatory).

From my exprience it is very difficult to get narow range of infarct area size in group due to variation in topography of vessel network and presicion of surgery procedure (I was workig without microscope). So, grouping by echography results with postmotem histological confirmation seems to be the best strategy in this model overwise results could be uncertain.

Obviously we should analyse whole heart slice but not a part of it. Since slice could be taken from different zones of the heart the resulting precision of estimation has low value. Variation will be higher due to other reasons (f.e. srinking of of original tissue and hypertrophy of distant myocardium during scar formation as demonstrated on attached picture). So I would pay attention to surgery procedure, position of the section used for analysis and reproducibility of staining results.

There are some other aproaches available for estimation of affected area by counting affected sectors instead of exact erea estimation. So it is better to decide what approach to use based on goal of project.

Have a nice experience with this demanding model!

Daniil R. Petrenyov thankyou for your explanation doctor, in your first answer, how you decided to use treshold to 214? not 230?should we decided ourself based on original image we have?thanks

Well, it is very good question. My decision was for demonstration only. If I need to do it again on big set of images I would use algorithm rather than decision. f.e. Huang's approach on bright background giving threshold around 234-237.