INTRODUCTION - Pusanbml.pusan.ac.kr/resources/introduction.pdf · 2012-03-21 · Color 2 • The...
Transcript of INTRODUCTION - Pusanbml.pusan.ac.kr/resources/introduction.pdf · 2012-03-21 · Color 2 • The...
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INTRODUCTION
Ho Kyung Kim, [email protected]
School of Mechanical EngineeringPusan National University
Fall 2011Introduction to Medical Engineering
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Color
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• The human retina contains three types of photoreceptor cone cells that transform the incident light with different color filters, hence three numbers are necessary and sufficient to describe any perceptible color.
Additive color mixing Subtractive color mixing
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• Visible light– electromagnetic radiation with wavelengths between 400 and 700 nm– each wavelength corresponds to a different color– purple = red + blue
• Monochromatic vs. polychromatic colors
• Monoenergetic vs. polyenergetic energies
• A color is characterized by a spectrum of different wavelengths.
• Chromatic light needs 3 descriptors to characterize it; hue, brightness & saturation.
• Achromatic light has only one descriptor; its intensity or gray value (saturation = 0).
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Digitalization = sampling + quantization
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Space
Intensity
Pixel pitch
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Digital images
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• Sampling, quantization (integer)• Dynamic range: the set of possible gray levels• Contouring: an artificial looking height• How many gray values are needed to produce a continuous-looking image?
8 bits/pixel 4 bits/pixel
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Sampling
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1 2 4 8
128643216
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Quantization
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8 bits 7 bits 6 bits 5 bits
1 bit2 bits3 bits4 bits
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Histogram
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• A probability distribution on the set of possible gray levels.
pixels ofnumber total gray value having pixels ofnumber )( vvhI
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• Consider n + 1 gray values: I0, I1, … , In physical intensities.
– Dynamic range,
• How many gray values are needed to produce a continuous looking image?
– Human eye cannot distinguish Ij and Ij+1 if they differ less than 1%, i.e., if Ij+1 1.01Ij.
– or
– n = 463 for = 100; n =694 for = 1000
– n = 4096 in most digital medical images
0IIn
0)01.1( II nn
01.1
001.1 loglog I
In n
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Image quality
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• Resolution– the ability to depict details
• Contrast– a measure of differences in brightness (or intensity) in adjacent regions
• Noise– random fluctuations in image intensity– inevitable due to the statistical nature of imaging (emission, detection, conversion)– Poisson statistics
• Artifacts– artificial image features such as dust or scratches in photos– should be avoided or at least understood their origin because they hamper the diagnosis or yield
incorrect measurements
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• Point-spread function (PSF), line-spread function (LSF)– full width at half maximum (FWHM)
• Optical transfer function (OTF)– Fourier transform of PSF or LSF– modulation-transfer function (MTF): the amplitude of OTF– Spatial frequency: the distinguishable number of line pairs
per millimeter (lp/mm)
Resolution
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Contrast
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• The amplitude of the Fourier transform of an image as a function of spatial frequency
Gopal and Samant, Med. Phys. 35:1, 270-79, 2008
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Image courtesy of M. J. Yaffe
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• Assuming that the spatial resolution is only determined by the pixel size of a detector, can you estimate the pixel size of the detector providing the following radiograph?
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Noise
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• Signal-to-noise ratio (SNR);
– A high SNR is no guarantee for a good perceptibility of image objects.
• Contrast-to-noise ratio (CNR)– to distinguish the neighboring objects– (SNR in terminology of images)– Wiener noise-power spectrum: Fourier transform of the autocorrelation of a “flat-field” image
rms
rms
NSSNR
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Artifacts
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