OpenCV for medical image analysis: open source pattern recognition

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From industry to medical imaging

OpenCV — Open Source Computer Vision Library — is one of the most widespread open source computer vision libraries in the world. Born in 1999-2000 within Intel Research under Gary Bradski’s initiative, it went through public alpha/beta releases between 2000 and 2006, and the first stable 1.0 release was published in October 2006. Version 1.1 is currently in preparation, with public release expected over 2008.

In 2008 the project’s support is taken up by Willow Garage, an industrial robotics spin-off founded in Menlo Park, funding the partial rewrite in C++ of key components and preparation of the 2.0 expected in 2009. The model is the shift from a corporate-funded project to a community project, keeping the original BSD licence and openness to external contributions.

The book “Learning OpenCV” by Gary Bradski and Adrian Kaehler, published by O’Reilly in September 2008, is the library’s reference manual — complete and didactically effective, contributing to further diffusion in academic and research environments.

The application domain

OpenCV is generalist: its original target is industrial vision, human-computer interaction, video surveillance, robotics, automation. It is not a library specifically for medical imaging — for that, the reference is ITK, discussed separately. But many of its base operations apply directly to biomedical images, and a growing number of clinical publications use OpenCV as implementation tool.

Its strength is its orientation to 2D on photographic-quality images — exactly the type of input of many non-volumetric diagnostic modalities: mammography, retinal/fundus imaging, dermatology, endoscopy, light microscopy, digital pathology. For 3D CT/MR volumes the natural choice remains ITK; for “natural” 2D medical images, OpenCV has much to offer.

Capabilities as of 2008

OpenCV 1.x includes:

Manipulation and pre-processing

• Reading/writing common image formats (not DICOM natively, but with external adapters)
• Colour conversions (RGB, HSV, LAB, grayscale), histograms, equalisation
• Filters: Gaussian, median, bilateral, Sobel, Laplacian, Canny (edge detection)
• Morphological operations: erosion, dilation, opening, closing, top-hat, watershed
• Transforms: Hough (lines and circles), Fourier, distance transform

Segmentation and analysis

• Thresholding (global, adaptive, Otsu)
• Contour detection and approximation
• Connected components
• GrabCut (foreground/background segmentation)
• Flood fill

Feature detection and description

• Corner detection: Harris, Shi-Tomasi (goodFeaturesToTrack), FAST
• Blob detection: MSER, SimpleBlobDetector
• SIFT and SURF — patent-covered (SIFT: University of British Columbia) so usable only in non-commercial contexts

Classification

• Support Vector Machines (wrapper over libsvm)
• K-nearest neighbours
• Boosted classifiers (AdaBoost)
• Random Trees
• Normal Bayes Classifier
• Neural networks (shallow multilayer perceptrons)
• Expectation-Maximization

Face detection

The implementation of Viola-Jones (2001), with cascades of Haar-like feature classifiers, is available out-of-the-box. Trained on human faces, it has been used as a starting point in many medical applications — training of custom cascades on lesions, microcalcifications, cells, via the provided training pipeline.

Applications in medicine

Biomedical literature from 2007-2008 starts citing OpenCV as implementation tool in several scenarios:

Mammography

• Pre-processing to enhance microcalcification contrast
• Detection of suspicious ROIs with custom Haar cascades
• Fibroglandular tissue segmentation
• Breast density analysis

Ophthalmic imaging

• Retinal vessel segmentation (Hough, reimplemented Frangi filters)
• Optic disc and fovea detection
• Diabetic retinopathy classification from geometric and chromatic features
• OCT (optical coherence tomography) analysis

Dermatology

• Pigmented lesion segmentation over skin background
• Feature extraction for ABCD criteria (Asymmetry, Border, Colour, Diameter)
• Nevus/melanoma classification with SVM/ensemble classifiers

Microscopy and digital pathology

• Automated cell counting (tissue microarray, smears)
• Nuclear segmentation
• Morphometric area and distribution measurement
• Reconstruction of whole slide images from tiles

Endoscopy

• Contrast and sharpness enhancement
• Polyp segmentation
• Artefact detection (bubbles, speculum)

Movement analysis and physiotherapy

• Gait analysis with video marker tracking
• Postural analysis
• Rehabilitative movement study

The typical flow

A clinical research project using OpenCV typically follows the pattern:

1. Acquisition — image in its original format (DICOM, TIFF, PNG, JPEG) loaded via specific library (e.g. DCMTK for DICOM) and converted into IplImage (C) or cv::Mat (C++ new API)
2. Pre-processing — enhancement, filtering, normalisation via OpenCV functions
3. Feature extraction — extraction of numerical descriptors (moments, texture, shape, colour)
4. Classification or clustering — with OpenCV algorithms or export to WEKA / R for more sophisticated analyses
5. Validation — cross-validation, ROC curves, comparison with clinician ground truth

Integration with existing research flows (MATLAB, R, Python) is supported by official bindings: Python (pycv), C# (emgucv), Java (javacv in development). The combination OpenCV + Python + NumPy is starting to spread as a MATLAB alternative in academic biomedical research.

Limits for medical applications

OpenCV has specific limits when applied to medicine:

• Non-volumetric — 3D operations on CT/MR volumes are outside the natural scope; for those scenarios ITK is the appropriate tool
• DICOM not native — lacks an integrated DICOM parser; has to be used alongside DCMTK, GDCM, pixelmed
• Numerical precision — traditional focus is performance, not precision; some critical operations (sub-pixel registration, high-quality interpolation) require additional care
• Regulatory certification — the BSD licence permits use in commercial products but certification as a medical device component requires extensive qualification (IEC 62304 software lifecycle, risk management); must be planned from day one
• Medical data variability — absence of standardised acquisitions (protocols, different scanners, lighting conditions in endoscopy/dermatology) creates generalisation issues that go beyond the library

The 2008 context

As of 2008 OpenCV is mid-transition:

• Ownership change from Intel to Willow Garage
• C++ rewrite under way, with a more idiomatic new API
• Arrival of the C++ API with cv::Mat as central structure (available in 2.0)
• Binding growth — the Python community is producing maintained wrappers
• Integration with robotic platforms like ROS (Robot Operating System), also in development at Willow Garage

For medical imaging, this means an evolving but stable library with a clear improvement trajectory. For those starting a research project in the coming months, the choice to stay on 1.x or migrate to 2.0 (when released) is a trade-off between immediate stability and future capabilities.

Outlook

OpenCV will become even more central in the following years:

• The arrival of deep learning frameworks (still distant: the first CNNs relevant to imaging like AlexNet will come in 2012) will change how features are extracted from images
• The Python binding will become the dominant way to use the library
• Integration with NumPy and scipy/scikit-learn will form the reference analytical stack for academic research

For medicine, OpenCV will remain a cross-cutting tool, useful in pre-processing phases and classical feature extraction, complementary to specialised tools (ITK for 3D volumetric, dcm4che for DICOM, WEKA for tabular classification). Choice of OpenCV for a clinical project depends on image nature and the overall pipeline — not a universal choice, but for the many non-volumetric 2D applications in medicine it is today the most mature and documented open source tool available.

References: OpenCV (opencv.org), OpenCV 1.0 (October 2006), 1.1 in preparation 2008. Gary Bradski, Adrian Kaehler, Learning OpenCV (O’Reilly, September 2008). BSD licence. Willow Garage as development sponsor from 2008. Viola-Jones face detection (2001).