# FFT bandpass filter and edge detection in ImageJ (with before/after examples)

> FFT in ImageJ step by step: Process › FFT, Bandpass Filter settings, stripe removal and Find Edges, with before/after pictures and when filtering is safe.

Published 2026-11-06 by LabCanvas team. https://www.labcanvas.com/blog/imagej-fft-bandpass-edge-detection

![ImageJ 1.54f on a synthetic nuclei image with horizontal stripes: the striped image, its FFT power spectrum, the bandpass filter, the filtered image with the stripes removed, Find Edges, and the edges thresholded and filled (68 objects)](https://www.labcanvas.com/blog/imagej/imagej-fft-bandpass-edge-detection-cover.webp)

To filter an image by frequency in ImageJ, open it and run **Process › FFT › Bandpass Filter…**: type the size above which structures count as background (**Filter large structures down to**, 40 pixels by default) and the size below which they count as noise (**Filter small structures up to**, 3 pixels), choose **Suppress stripes** if the image has horizontal or vertical stripes, and click **OK**. To see the frequencies first, use **Process › FFT › FFT**. For edge detection, run **Process › Find Edges** (a Sobel filter), ideally on the bandpassed image.

Filtering is fine for **finding** objects (thresholding, counting, outlining). It is never fine for **measuring intensity**: on the sample below the bandpass filter lowered one nucleus by 17.8% and another by 0.7%.

> **What you need:** ImageJ 1.x or Fiji (every command here is in both). Any grey image; the example is the synthetic nuclei sample (16-bit, 512 × 512 pixels, 0.325 µm per pixel, 70 nuclei, a background brighter at the lower left), plus a copy with horizontal stripes added, made for practice. Time: about 10 minutes.

## What the FFT shows

The Fourier transform rewrites an image as a sum of waves. Each point of the **power spectrum** is one wave: its distance from the centre is how fine it is (centre = large, slow changes such as the background; edge = fine detail and pixel noise), and its direction is the direction of the wave. A filter in frequency space keeps some of those waves and throws the rest away, then turns the result back into an image.

That is why the bandpass filter is described in sizes. "Filter large structures down to 40 pixels" removes every wave longer than about 40 pixels; "Filter small structures up to 3 pixels" removes every wave shorter than about 3 pixels. What is left is the band in between, where nuclei, cells and particles live.

## How do I use the FFT bandpass filter in ImageJ? Step by step

### Step 1: Open the image

Click **File › Open…** and choose the image. Look at it before you filter: here there are fine horizontal stripes (one every 8 pixels) and the background is more than twice as bright at the lower left (1446 against 639 grey values in the opposite corner).

![ImageJ window with the striped synthetic nuclei image, the stripes enlarged in an inset](https://www.labcanvas.com/blog/imagej/imagej-fft-bandpass-edge-detection-step1-open.webp "Step 1 in ImageJ 1.54f: the synthetic nuclei image with stripes added. The inset shows the stripes 1; the background is brighter towards the lower left 2.")

### Step 2: Look at the power spectrum

Choose **Process › FFT › FFT**. A new window opens with the power spectrum. The bright blob in the centre is the large, slow changes; the grainy edge is noise. The stripes are two small bright dots above and below the centre, on the vertical axis: a pattern that repeats from top to bottom shows up on that axis.

![ImageJ striped nuclei image beside its FFT power spectrum, with the two stripe dots enlarged](https://www.labcanvas.com/blog/imagej/imagej-fft-bandpass-edge-detection-step2-fft.webp "Step 2: Process › FFT › FFT. The centre of the spectrum holds the large structures 1; the stripes are two dots on the vertical axis, 64 pixels from the centre, enlarged on the right 2; fine detail and noise are at the edges 3.")

You do not have to use this window to filter. It tells you what is in the image: a dot or a line away from the centre is a regular pattern (stripes, a grid, mains hum on a camera), and you can then decide how to remove it.

### Step 3: Open the Bandpass Filter and type the two sizes

Select the image window again and choose **Process › FFT › Bandpass Filter…**.

- **Filter large structures down to**: a little more than your largest object. Nuclei here are about 20 to 30 pixels across, so the default **40** keeps them and removes the slow background.
- **Filter small structures up to**: a little less than the finest detail you want to keep. The default **3** removes pixel noise but keeps the nucleus edges.
- Keep **Autoscale after filtering** and **Saturate image when autoscaling** ticked to see the result well (they change the values; see below).

![ImageJ FFT Bandpass Filter dialog with 40 and 3 pixels typed in](https://www.labcanvas.com/blog/imagej/imagej-fft-bandpass-edge-detection-step3-bandpass.webp "Step 3: Process › FFT › Bandpass Filter… Type 40 in Filter large structures down to 1 and 3 in Filter small structures up to 2; keep Autoscale ticked to see the result 3.")

### Step 4: Choose the stripe direction

If the image has stripes, set **Suppress stripes** to their direction: **Horizontal** for stripes that run left to right, as here. Leave **Tolerance of direction** at 5%. Tick **Display filter** if you want to see what is removed. Click **OK**.

![ImageJ Bandpass Filter dialog with Suppress stripes set to Horizontal](https://www.labcanvas.com/blog/imagej/imagej-fft-bandpass-edge-detection-step4-stripes.webp "Step 4: Suppress stripes set to Horizontal 1, Tolerance of direction 5% 2.")

### Step 5: Check the result

The filtered image replaces the original (use **Edit › Undo** or **Image › Duplicate…** first if you want both). With **Display filter** ticked, ImageJ also shows the filter: white is kept, black is removed. The black centre is the background you asked to remove, the dark rim is the noise, and the thin vertical line is the stripe cut.

![ImageJ before and after the bandpass filter, with the filter shown on the right](https://www.labcanvas.com/blog/imagej/imagej-fft-bandpass-edge-detection-step5-result.webp "Step 5: the striped original 1, the result of Bandpass 40/3 with Horizontal stripes suppressed 2, and the filter 3. The stripes are gone apart from a faint trace, and the lower-left background is now as dark as the rest.")

Measured on the unscaled result, the background corners went from 1446 and 639 grey values (2.3 times apart) to 1292 and 1193 (1.08 times apart).

## Which sizes should I use? Before and after

The two sizes are the only settings that matter. The grid shows them on the nuclei sample without stripes:

![Four versions of the nuclei image: original, bandpass 40/3, bandpass 10/3 and bandpass 40/10](https://www.labcanvas.com/blog/imagej/imagej-fft-bandpass-edge-detection-grid.webp "ImageJ 1.54f on the synthetic nuclei sample: the original; Large 40, Small 3 even background, nuclei unchanged in shape; Large 10, Small 3 too small: nuclei become rings with dark halos; Large 40, Small 10 too much smoothing: nuclei blur together.")

| Large / small (pixels) | What happens | Otsu count | % area |
|---|---|---|---|
| Original | Uneven background | 70 | 10.21 |
| 40 / 3 (default) | Background flattened, nuclei unchanged | 70 | 9.70 |
| 10 / 3 | Large below the nucleus size: centres hollowed out, dark halos | 70 | 9.15 |
| 40 / 10 | Small too large: nuclei soften and spread | 70 | 10.76 |

The count stays at 70 because these nuclei are bright and well apart; the % area shows the shapes changing. On a crowded or dim image, the wrong sizes merge or lose objects. The rule: **large** above your biggest object, **small** below your finest detail.

### Horizontal or vertical stripes: which one?

ImageJ names the stripes, not the line in the spectrum. Horizontal stripes (rows of light and dark) need **Horizontal**:

![Striped nuclei image filtered with no stripe suppression, Horizontal and Vertical](https://www.labcanvas.com/blog/imagej/imagej-fft-bandpass-edge-detection-stripes-grid.webp "The striped image; Bandpass 40/3 with Suppress stripes None stripes remain; Horizontal stripes removed; Vertical wrong: the stripes stay and new vertical streaks appear.")

## How do I detect edges in ImageJ?

**Process › Find Edges** is ImageJ's edge detector. It uses the **Sobel** operator: two 3 × 3 kernels measure the change in grey value across and down the image, and each pixel becomes the square root of the sum of the two squared. A sharp boundary becomes a bright line; a flat area becomes dark. It has no settings.

### Step 1: Find Edges on the original

Open the image and choose **Process › Find Edges** (make a copy first with **Image › Duplicate…**; it replaces the image). Every nucleus becomes a ring; bright nuclei give bright rings, dim nuclei faint ones.

![ImageJ nuclei image beside its Find Edges result](https://www.labcanvas.com/blog/imagej/imagej-fft-bandpass-edge-detection-edges-step1.webp "Edges step 1: the original 1 and Process › Find Edges 2. Edges are bright where the grey value changes fast; dim nuclei give faint rings.")

### Step 2: Bandpass first, then Find Edges

Sobel works on 3 × 3 pixels, so noise and stripes become edges too. Run the bandpass filter from the first part (40/3) and then **Process › Find Edges**. The rings are now even and clean, dim nuclei included, because the bandpass evened out the background and the autoscale spread the values.

![ImageJ bandpassed nuclei image beside its Find Edges result](https://www.labcanvas.com/blog/imagej/imagej-fft-bandpass-edge-detection-edges-step2.webp "Edges step 2: the bandpassed image 1 and Find Edges on it 2: even rings around every nucleus.")

### Step 3: Threshold the edges

Choose **Image › Adjust › Threshold…**, pick **Otsu**, tick **Dark background**, and check that every ring is red. On this image Otsu sets the threshold at 25186 (11.3% of the pixels). Click **Apply** (or **Process › Binary › Convert to Mask**).

![ImageJ Threshold window with Otsu and Dark background on the edge image](https://www.labcanvas.com/blog/imagej/imagej-fft-bandpass-edge-detection-edges-step3.webp "Edges step 3: Image › Adjust › Threshold… on the edge image, Otsu 1, Dark background ticked 2, threshold 25186 3.")

### Step 4: Fill the rings and count

Run **Process › Binary › Fill Holes** to turn the rings into solid objects, then **Analyze › Analyze Particles…** with **Summarize** ticked.

![ImageJ outlines of the filled edge objects and the Summary table with 68 objects](https://www.labcanvas.com/blog/imagej/imagej-fft-bandpass-edge-detection-edges-step4.webp "Edges step 4: Fill Holes, then Analyze Particles from 7 µm²: 68 objects 1 with a mean size of 65.5 µm² 2. Two touching nuclei merged into one 3.")

The result is honest about edges: **68 objects for 70 nuclei**, and a mean size of **65.5 µm² against 40.4 µm²** from a plain threshold. The Sobel ring is several pixels wide and sits half outside each nucleus, so objects made from edges are too big, and touching nuclei share one ring. Edges are good for seeing boundaries, finding shapes and guiding a watershed; for counting and sizing round objects, a threshold of the bandpassed image is better.

![Six edge images: Find Edges on the original, after bandpass, after Gaussian blur, on the striped image, after stripe removal, and the filled mask](https://www.labcanvas.com/blog/imagej/imagej-fft-bandpass-edge-detection-edges-grid.webp "Find Edges in ImageJ 1.54f, before and after filtering. On the striped image without filtering, Otsu on the edges finds 74 objects, some of them pieces of stripe; after stripe removal it finds 68.")

A **Gaussian blur** first (**Process › Filters › Gaussian Blur…**, sigma 2) also cleans the edges, but it does not flatten the background, so the dim nuclei stay faint. Fiji has more edge detectors through update sites (for example a Canny-type detector in FeatureJ on the ImageScience site); plain ImageJ has Find Edges only.

## When is filtering OK, and when is it not?

The bandpass filter is a **segmentation** tool. It is the right step before a threshold, a particle count, a watershed or an outline. It is the wrong step before any **intensity** measurement: mean grey value, integrated density, CTCF, a ratio, a western blot band. On the sample:

| Nucleus | Original mean | After Bandpass 40/3 (no autoscale) | Change |
|---|---|---|---|
| Brightest (lower left) | 4932 | 4055 | −17.8% |
| Dimmest (upper right) | 3017 | 2995 | −0.7% |
| Ratio bright / dim | 1.64 | 1.35 | |

The filter removes the slow background unevenly and trims the peak of small bright objects, so values change by different amounts in different places. With **Autoscale** on (the default) the values are also stretched to the full 16-bit range: the same two nuclei read 52,567 and 38,692. Neither version is a measurement of the sample.

The safe way:

1. **Image › Duplicate…** the original. Filter the copy.
2. Threshold and segment the filtered copy (mask or ROIs in the ROI Manager).
3. In **Analyze › Set Measurements…** set **Redirect to** the original image, or apply the ROIs to the original, and measure there.

If the background really must come off before measuring, use **Process › Subtract Background…** (rolling ball) on the original and say so in the methods; see [rolling-ball background subtraction](https://www.labcanvas.com/blog/rolling-ball-background-subtraction).

## Pitfalls

- **Large size below the object size.** The filter removes the inside of the objects and leaves rings with dark halos (the 10/3 tile above). Keep **large** above the biggest object.
- **Wrong stripe direction.** Horizontal stripes need **Horizontal**. The wrong one adds streaks.
- **Measuring the filtered image.** See above. Reviewers will ask.
- **Filtering a stack slice only.** For a stack, tick **Process entire stack** in the dialog, or only the current slice changes.
- **Find Edges on a noisy image.** Every noisy pixel becomes an edge. Filter first.
- **8-bit after autoscale.** Converting the filtered 16-bit image to 8-bit after autoscale is fine for segmentation; it is one more reason not to measure intensity on it.

## Reporting it

> Images were bandpass filtered in ImageJ 1.54f (Process › FFT › Bandpass Filter; structures larger than [40] and smaller than [3] pixels removed, [horizontal stripes suppressed, tolerance 5%]) for segmentation only. Objects were thresholded (Otsu) on the filtered image; all intensities were measured on the original, unfiltered images.

Cite ImageJ (Schneider et al. 2012). If you show filtered images in a figure, say so in the legend.

## Switch to LabCanvas: the same filter in a few clicks

[LabCanvas Image Analysis](https://www.labcanvas.com/analysis.html) has ImageJ's bandpass filter in the browser, with nothing to install.

1. Click **Open an image** (or use a sample) and look at the **Image check**: it flags uneven illumination before you filter.
2. In the **Process** tab › **Enhance**, choose **FFT bandpass**.
3. Type the large and small sizes in pixels, and choose horizontal or vertical stripes if needed; apply it **As a new image** to keep the original.
4. Threshold and count the copy in the **Count** tab, and measure intensities on the original.

![Image check in LabCanvas Image Analysis](https://www.labcanvas.com/analysis-examples/check.webp "Image check: All fine: nothing clipped brightest value 3,688, even illumination the background varies by 1.06×, sharpest at slice 8 of 15.")

The app's bandpass code reproduces ImageJ's: run on the same two images (40/3, and 40/3 with horizontal stripes), its output matched ImageJ 1.54f's to within 1 grey value out of 65,535, with 99.7% of the pixels identical.

Why switch for this job:

- **No install**: the same filter on any computer or tablet, in the browser.
- **Built-in guard rails**: the Enhance panel says these steps are for seeing and segmenting, not for measuring intensities, and the image check flags uneven light first.
- **Validated**: every measurement is checked against reference software in [more than 4,000 automated tests](https://www.labcanvas.com/image-analysis-validation).

ImageJ stays better here in one way: LabCanvas has no separate Find Edges command and no editable power spectrum, so for Sobel edges or hand-painted FFT masks use ImageJ or Fiji.

[Open Image Analysis](https://www.labcanvas.com/analysis.html): free with a LabCanvas account (downloading files is part of LabCanvas Pro). ImageJ and Fiji are independent open-source projects; LabCanvas is not affiliated with them.

## References

- Schneider CA, Rasband WS, Eliceiri KW (2012). NIH Image to ImageJ: 25 years of image analysis. *Nature Methods* 9, 671–675. [doi:10.1038/nmeth.2089](https://doi.org/10.1038/nmeth.2089)

## Frequently asked questions

### How do I do an FFT in ImageJ?

Open the image and choose Process › FFT › FFT. ImageJ shows the power spectrum (the log-scaled magnitude of the Fourier transform) as a new 8-bit window, with the lowest frequencies in the centre. To filter, use Process › FFT › Bandpass Filter…, or paint over parts of the spectrum (black removes, white keeps) and run Process › FFT › Inverse FFT.

### What do the FFT Bandpass Filter settings mean?

Filter large structures down to (default 40 pixels) removes anything larger than that size, such as an uneven background. Filter small structures up to (default 3 pixels) removes anything smaller, such as pixel noise. Suppress stripes removes horizontal or vertical stripes, Autoscale stretches the result to the full range, and Display filter shows the filter itself.

### How do I remove stripes from an image in ImageJ?

Use Process › FFT › Bandpass Filter… with Suppress stripes set to Horizontal for horizontal stripes or Vertical for vertical ones, and Tolerance of direction at 5%. Stripes show up in the power spectrum as bright dots on one axis; the filter cuts that axis out. Choosing the wrong direction leaves the stripes and adds new artefacts.

### How does Find Edges work in ImageJ?

Process › Find Edges is a Sobel edge detector: two 3 × 3 kernels measure how fast the grey value changes across and down the image, and each pixel becomes the square root of the sum of their squares. Edges come out bright and flat areas dark. Smooth or bandpass the image first, or the noise becomes edges too.

### Can I measure intensity after an FFT bandpass filter?

No. The filter changes the values: on our sample it lowered the brightest nucleus by 17.8% and the dimmest by 0.7%, so their ratio fell from 1.64 to 1.35, and Autoscale then rescales everything. Use the filtered image only to find the objects, and measure on the original (Set Measurements › Redirect to).

### Is there an easier way without installing ImageJ?

LabCanvas Image Analysis runs in the browser and has the same FFT bandpass (Process › Enhance › FFT bandpass), with large and small sizes and stripe removal. It is free with a LabCanvas account; it has no separate Find Edges command.

## Related links

- [Rolling-ball background subtraction](https://www.labcanvas.com/blog/rolling-ball-background-subtraction): The usual fix for an uneven background
- [ImageJ threshold methods compared](https://www.labcanvas.com/blog/imagej-threshold-methods): What to do with the filtered image next
- [How to count cells in ImageJ](https://www.labcanvas.com/blog/count-cells-imagej): Threshold, watershed and Analyze Particles
- [d-spacing from HRTEM lattice fringes](https://www.labcanvas.com/blog/d-spacing-hrtem-lattice-fringes): The FFT used for measuring, not filtering
- [Measure fluorescence intensity in ImageJ](https://www.labcanvas.com/blog/measure-fluorescence-intensity-imagej): Why intensities need the raw image
- [Image Analysis: the complete guide](https://www.labcanvas.com/blog/image-analysis-complete-guide): Every tool in LabCanvas Image Analysis