Post on 30-Dec-2015
description
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:
– 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
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
Morphologya : 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
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?
Morphology analysis
raw image segmentation edge/skeleton detection properties
– cell area
– kernel area
– cell hue
– kernel hue
– skeleton radius
– kernel min thickness
– kernel max thickness
– number of kernel segments
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
Classification algorithm
acquisition
• Digital cameras
• Video capturing and digitizing
preprocessing
• Segmentation
• Contrast stretching
• Brightness enhancement
features extracting classification
• Wavelet transformation
• AMT
• PCA
• PLS-DA
Features vector building
• Wavelet transformation– 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)
Features vector: wavelet transformation
Raw signal
Smoothed Details
H G
Smoothed Details
H G
…
Gr
Hr
diag
hor
ver
HrHc HrGc GrHc GrGc
r — rows
c — columns
H – gives smoothing signal
G – gives the details
1D signal 2D signal
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
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 sensitive for changing of typical scales of objects on images
Features vector: AMT
0 20 40 60 80 100 120 140 160 1800
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Step 2: Sampling
Step 3: Measure angle and calculation mean angle for all points
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 SM compose mean angle
spectrum { MAS0,…,MASM }
Example of MA spectrum
Spectrum can be regarded as a vector of images features on set of scales
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
Preliminary PCA and PLS (calibration set)
AMT Wavelet transform
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Preliminary PLS (test set)
AMT Wavelet transform
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Unfolded images profiles
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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
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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