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Using black and white models for classification of medical images

Using black and white models for classification of medical images. Sergei Kucheryavski, Altai State University, Russia svk@asu.ru. Prehistory: analysis of medical data. Children's hospital of Altai region:

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Using black and white models for classification of medical images

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  1. Using black and white models for classification of medical images Sergei Kucheryavski, Altai State University, Russia svk@asu.ru

  2. Prehistory: analysis of medical data Children's hospital of Altai region: • analysis of frequencies of different diseases occurring in patients with perinatal lesions of the central nervous system • analysis and recognition of blood cells • analysis and recognition of marrow cells Department of urology and nephrology of AMU • analysis of ultrasound and X-ray tomograms of urolitas • determination of operable/therapeutic state of disease using ultrasound and X-ray tomograms of tissues

  3. Prehistory: existent methods of analysis • Based on the conceptual models • geometrical size of objects and distance between them • geometrical area of objects or segments • hue and color intensity • … • Rigid requirements to the raw data • low-noise • high level of contrast and intensity Morphology a : a branch of biology that deals with the form and structure of animals and plants b : the form and structure of an organism or any of its parts Merriam-Webster Online Dictionary

  4. Presummary and questions to answer • Habitual methods, based on the hard model of studied objects, are very spread in medicine • Soft models based image analysis approach usually allows to analyze images with middle and low quality including noised images • Therefore: • Is it possible to use soft model approach for medicine purposes? • Will such approach give results with acceptable quality? • Are there any advantage in using soft model approach in comparison with traditional one?

  5. Blood cells formation red cells white cells

  6. Basic white cell types row segmented Neutrophils Monocyte Lymphocyte

  7. Morphology analysis • cell area • kernel area • cell hue • kernel hue • skeleton radius • kernel min thickness • kernel max thickness • number of kernel segments raw image segmentation edge/skeleton detection properties

  8. Blood cells analysis software • Conditions • rigid requirements to the image quality • sensitive to presence of noise • rigid requirements to the smear quality • Effects • poor results for middle and low quality images • rigid requirements to the equipment (microscopes, cameras, etc) • rigid requirements to chemical for smear preparation • As a result • highly recommended to use such software with equipment and chemical from the same producer • price for software only: $2 000 – 10 000 • price for equipment: $50 000 – 100 000

  9. Ordinary and good pictures of blood

  10. Classification algorithm acquisition preprocessing features extracting classification • Digital cameras • Video capturing and digitizing • Segmentation • Contrast stretching • Brightness enhancement • Wavelet transformation • AMT • PCA • PLS-DA

  11. Features vector building • Wavelettransformation • transforms image from spatial to frequency-spatial domain • good results in different areas of image recognition and analysis • quick and simple algorithm • AMT • transforms image from spatial to scale domain • good result in classification of both heterogeneous images and textures • simple algorithm but relatively slow for big images (1-4 seconds in comparison with Wavelet transformation –- 0.2-0.8 seconds)

  12. Raw signal H G Smoothed Details Hr H G Gr Smoothed Details HrHc HrGc GrHc GrGc … hor r — rows c — columns ver diag Features vector: wavelet transformation H – gives smoothing signal G – gives the details 1D signal 2D signal

  13. Features vector: wavelet transformation For feature vector we calculate metrics of horizontal, vertical and diagonal details: Feature vector — [ f(dh1),f(dv1), f(dd1),…,f(dhm),f(dvm),f(ddm) ] 1…m level of wavelet transform dh, dv,dd horizontal, vertical and diagonal details f() metrics function Useful metrics: • Energy • Standard deviation • Moments

  14. Features vector: AMT • Was developed by Robert Andrle as a substitute of fractal analysis for the purpose of complexity of geomorphic lines investigation (R. Andrle, Math. Geol., 16, 83-79, (1996)) • Was introduced into chemometrics as generic approach for analysis of measurement series by Esbensen et al(K.H. Esbensen, K, Kvaal, K.H. Hjelmen, J. Chemom., 10, 569-590, (1996)) • Properties • transforms the 2D image into 1D spectra without losses the structure information • highly sensitivefor changing of typical scales of objects on images

  15. Features vector: AMT Step 1: Unfolding

  16. Features vector: AMT Step 2: Sampling Step 3: Measure angle and calculation mean angle for all points

  17. Features vector: AMT Step 4: Change radius S and repeat step 3 for mean angle vector (spectrum) building • Mean angle values (MAS) for each S from S0 to SMcompose mean angle spectrum { MAS0,…,MASM } Example of MA spectrum Spectrum can be regarded as a vector of images features on set of scales

  18. Objects for investigations • Calibration set • 60 samples • 2 classes • Samples were taken from different people • Ordinary microscope and cheap VGA camera were used • Test set • 96 samples • Samples were taken from different people • Samples were taken in other day then calibration set • Ordinary microscope and cheap VGA camera were used

  19. Objects for investigations

  20. Preliminary PCA and PLS (calibration set) AMT Wavelet transform

  21. Preliminary PLS (test set) AMT Wavelet transform

  22. Surf of blood cells

  23. Spiral unfolding

  24. Unfolded images profiles

  25. Preliminary analysis

  26. PLS-DA results • Prediction of calibration set • 60 samples • Samples were taken in different days and from different people • Prediction of test set • 96 samples • Samples were taken in different days and from different people

  27. Summary • Conclusions • Hard-modeling approach that is used to image analysis effective only for high-quality images • The soft-modeling approach of image classification was applied to the task of blood cell type recognition on low-quality images • The effectiveness of recognition was 96-97% that allows to speak about advantages of such approach • To be continued • Analysis of middle resolution images (1-2 Mp) • Approximation of cells by ellipse curve and ellipse-like unfolding • Use other methods for analysis of image profiles

  28. Acknowledgements • Alexey Pijanzin, docent, doctor of Children's hospital of Altai region • Ivan Belyaev, M.S. student of Altai State University • Sergei Zhilin, PhD, docent of Altai State University

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