Image segmentation and particle analysis algorithm services
For particle, blob, region and contour extraction tasks, it provides sample evaluation and project adaptation for threshold segmentation, connected domains, adhesion separation, clustering, skeleton and boundary processing. Segmentation results can continue to be used for counting, size, morphology and statistical analyses.
Applicable objects and analysis tasks
Ideal for image tasks where the target and background have exploitable differences in grayscale, color, texture or geometry, such as powders and particles, microscopic areas, holes and spots, coated areas, fibers and general foreground extraction.
- Generate binary or multi-region masks, recording thresholds and preprocessing parameters.
- Extract connected regions, areas, locations, circumscribed geometry and contours.
- Seed and watershed separation of adjacent or cohesive objects.
- Extract skeletons, paths, distance fields, and boundary candidates.
- Organize candidate targets using blob detection or density clustering.
- Output counts, size distributions, and abnormal areas under explicit rules.
When the task involves defect category or semantic judgment, rules or learning models can be added based on universal segmentation, and training data, labels, weight permissions, and target product verification can be confirmed.
Input, output and statistical caliber
| Project | Main content | Need fixed rules |
|---|---|---|
| input image | Grayscale, color or confirmed multi-channel images | Bit depth, compression, pixel scaling, ROI and preprocessing |
| Segmentation results | Binary or label masks, thresholds, quality states | Prospect Definition, Boundary Attribution and Undecidable Conditions |
| object result | Connected domains, locations, areas, contours and shapes | Connectivity rules, minimum area, boundary object processing |
| Adhesion separation | Seeds, instance regions, and detached states | Over-split, under-split and merge rules |
| Statistical results | Count, size distribution, proportion and abnormal records | Statistical denominators, units, truncated objects, and repeated objects |
If physical dimensions are required, scale or calibration must be provided; if conformity determination is required, product specifications, reference markings, and boundary samples must be provided. The resulting interface should preserve mask or reviewable profiles, parameter versions, and failure reasons.
Technical route and key points for selection
| route | suitable conditions | Main limitations |
|---|---|---|
| Global vs. local thresholds | Grayscale distribution or local contrast can distinguish | Lighting, background, and scale changes can alter results |
| Connected domains and morphological processing | A binary mask is available | Object categories and filtering rules need to be defined separately |
| Distance field and watershed | There is an available distance peak or seed for the object | Seed quality determines over- and under-partitioning |
| Skeleton and path analysis | Elongated structures or ordered boundaries can be extracted | Splits, noise and breaks require specialized treatment |
| Spot detection and density clustering | Scale and density definable | Parameter changes will affect the number of targets and groupings |
| Region, color and energy segmentation | Foreground cues, colors or area energies available | Initialization, color space and boundary condition sensitive |
The typical process is: image quality inspection → color or grayscale preprocessing → candidate segmentation → morphology and adhesion processing → object screening → size and statistics → results, masks and status output.
Sample and Imaging Requirements
Representative data should cover target size, density, color or grayscale, background, illumination, focus, contamination, adhesions, occlusions, and field edges, and include both normal, borderline, and failure samples.
The original image needs to retain bit depth and acquisition information. Microscopy or high-magnification tasks should account for pixel scale, depth of field, slide or container effects; online tasks should account for motion blur, triggering, tape speed, and camera exposure.
The reference annotation should explain the object definition, boundary rules, how glued objects are counted, and whether truncated objects are included in the statistics. Labeling differences between different people or different rules need to be dealt with before acceptance.
Verification methods and applicable conditions
The project uses validated annotation data to separately verify pixel segmentation, object matching, counting, size, adhesion separation, and failure handling. Product categories, defect types and conformity determinations are defined individually according to customer rules.
The detectable size, counting error, segmentation IoU, adhesion separation effect and processing beat are tested on agreed imaging, real samples and target platforms, and are recorded according to object-level, pixel-level or image-level calibers.
Project implementation and delivery
- Statistical object confirmation: Define foregrounds, objects, dimensions, glue, boundaries, and undecidable rules.
- Sample and labeled baseline: Fixed representative data, reference annotations and indicators, and compared segmentation technical routes.
- Software and interface adaptation: Complete image access, parameter recipe, mask and object output, status, log and export.
- Independent data acceptance: Cover density, size, background and abnormal changes, record segmentation, counting, time consumption and failure results.
Deliverables may include algorithm programs or libraries, configurations, interface descriptions, reviewable masks, verification reports, and release notes. Camera optics, sample preparation, annotation production, learning model training and hardware procurement are performed within the confirmed scope.
Acceptance method
| Acceptance tasks | Optional indicators | The caliber must be clear |
|---|---|---|
| split | IoU, Dice, pixel precision and recall | Categories, ignore areas, boundary tolerances, and statistical hierarchies |
| Objects and Counting | Object precision, recall, counting error | Match thresholds, glue, truncation and duplicate objects |
| Size and distribution | Size error, distribution differences, and effective sample rate | Calibration, object definitions, units and reference methods |
| Failure handling | Untargeted, low quality, overly dense, occluded, and out-of-range samples | Rejection status, manual review and whether to enter statistics |
| Running performance | Single frame and batch time consumption, memory and result update rate | Resolution, platform, task composition and concurrency |
Indicators are selected based on product risk and do not require all projects to mechanically apply the same set of values. Object-level, pixel-level, and image-level metrics cannot be mixed.
Authorization, maintenance and changes
Run licenses can be agreed upon by device, scenario, product family, and configuration version. The original scene is optimized using newly added representative samples to perform regression of the old and new versions, and the updated version is released after passing the agreed verification.
Changing lighting, magnification, camera, background, target category, statistical caliber, or product family may affect segmentation and require changes in evaluation. Initial model training, bulk annotation and new defect categories are listed separately in the contract.
FAQ
Can the particles be counted accurately when they stick together?
It depends on the degree of adhesion, imaging resolution and available seeds. Watershed or distance field methods can be evaluated; severely occluded and indistinguishable objects require improved imaging or added separation constraints.
Can the accuracy be determined by providing only a few pictures?
cannot. A small number of sample images can be used for preliminary screening of feasibility. The accuracy and counting errors need to cover normal changes, boundary samples and failure conditions, and use independent annotation data statistics.
Do I need to train a deep learning model?
It doesn’t have to be adopted uniformly. When the grayscale, color and shape are stable, traditional segmentation can be evaluated first; if the difference between the target and the background is complex or the category semantics is important, then confirm the training data, weight permission and learning model route.
Can the output be directly used as a quality judgment?
Quality judgment can only be accessed after the product specifications, judgment caliber, calibration and acceptance indicators are clear. Generic segmentation results are not themselves pass or fail conclusions.
Define the Development Scope with Samples and Device Requirements
Image segmentation and particle analysis algorithm services
Please describe the task object, original picture or video, target indicators, device platform, interface and sample situation.
Phone: 13910119357xuzhiyang0928@gmail.com