382
Index
detection process, 295
one-dimensional case, 297
thermal equilibrium, 295
registration, 306–307
shear and strain noise, 305
Siemens machines, 284
slice selection algorithm, 292–293
spontaneous neural fluctuations, 304–305
subject motion, 304
T 1 -and T 2 -weighted imaging, 294
thermal noise, 304
Magnetoencephalogram (MEG), 241–242
Mask processing
low-pass filter, 48–50
3 × 3 mask, 47–48
median filter, 50–52
sharpening spatial filters, 53–58
derivative filters, 56–58
high-boost filters, 54–56
high-pass filter, 53–54
MATLAB ® , 99, 149–150
Maximum likelihood estimation (MLE), 138–140
Median filter, 50–52
Meninges, 199
Meningitis, 209
Mexican hat mother wavelet, 87
Motor end plate, 218
MRI, see Magnetic resonance imaging (MRI)
Multidimensional signals, 4
Multilayer sigmoid neural network, see Sigmoid
neural networks
Muscles
categories, 217–218
contraction, 220–221
force, 221–222
motor unit, 218–220
Myocardial infarction, 184–185
Myotony, 229
N
Natural pacemakers and conduction system, 174
Near-field scanning optical microscopy (NSOM),
362–364
Neural networks
MATLAB ® , 149–150
perceptrons, 140–144
sigmoid neural networks, 145–149
Neuromuscular diseases
abnormal enervation, 226–227
abnormal neuromuscular transmission,
228–229
defects in muscle cell membrane, 229
pathological motor units, 227–228
Neurotransmitter, 218
NSOM, see Near-field scanning optical
microscopy (NSOM)
O
Object segmentation
using luminance thresholding, 73–75
quad-tree algorithm, 76–77
region growing, 75–76
One-dimensional continuous Fourier transform
convolution, 23–24
differentiation, 26
frequency domain, 15–16
impulse function, 17–19
linear systems analysis, 24–26
properties, 23
scaling property, 26
signal shift, 23
time signal, 21–22
unit pulse function, 20–21
One-dimensional continuous wavelet transform
(CWT)
of a time signal, 86
Daubechies (dbX) wavelets, 87–88
inverse continuous wavelet transform
(ICWT), 87
Mexican hat mother wavelet, 87
other mother wavelets, 88
One-dimensional discrete Fourier transform
(1-D DFT), see Discrete Fourier
transform (DFT)
One-dimensional discrete wavelet transform
(1-D DWT)
definition, 88–89
on discrete signals, 90–94
synthesis equation, 89
One-dimensional (1-D) signal, 3
Optical microscopy
design, 356
disadvantages, 357
geometry, 356
limitation, 356–357
Rayleigh criterion, 357
representative image, 357–358
resolution, 357
P
Parkinson’s disease, 209
Perceptrons, 140–144
PET, see Positron emission tomography (PET)
Petit mal, 207
Point detection methods, 70–71
Point processing
bit-level slicing, 43–44
contrast enhancement, 41–43
definition, 40
histogram equalization, 44–47
Positron emission tomography (PET)
applications
Index
detection process, 295
one-dimensional case, 297
thermal equilibrium, 295
registration, 306–307
shear and strain noise, 305
Siemens machines, 284
slice selection algorithm, 292–293
spontaneous neural fluctuations, 304–305
subject motion, 304
T 1 -and T 2 -weighted imaging, 294
thermal noise, 304
Magnetoencephalogram (MEG), 241–242
Mask processing
low-pass filter, 48–50
3 × 3 mask, 47–48
median filter, 50–52
sharpening spatial filters, 53–58
derivative filters, 56–58
high-boost filters, 54–56
high-pass filter, 53–54
MATLAB ® , 99, 149–150
Maximum likelihood estimation (MLE), 138–140
Median filter, 50–52
Meninges, 199
Meningitis, 209
Mexican hat mother wavelet, 87
Motor end plate, 218
MRI, see Magnetic resonance imaging (MRI)
Multidimensional signals, 4
Multilayer sigmoid neural network, see Sigmoid
neural networks
Muscles
categories, 217–218
contraction, 220–221
force, 221–222
motor unit, 218–220
Myocardial infarction, 184–185
Myotony, 229
N
Natural pacemakers and conduction system, 174
Near-field scanning optical microscopy (NSOM),
362–364
Neural networks
MATLAB ® , 149–150
perceptrons, 140–144
sigmoid neural networks, 145–149
Neuromuscular diseases
abnormal enervation, 226–227
abnormal neuromuscular transmission,
228–229
defects in muscle cell membrane, 229
pathological motor units, 227–228
Neurotransmitter, 218
NSOM, see Near-field scanning optical
microscopy (NSOM)
O
Object segmentation
using luminance thresholding, 73–75
quad-tree algorithm, 76–77
region growing, 75–76
One-dimensional continuous Fourier transform
convolution, 23–24
differentiation, 26
frequency domain, 15–16
impulse function, 17–19
linear systems analysis, 24–26
properties, 23
scaling property, 26
signal shift, 23
time signal, 21–22
unit pulse function, 20–21
One-dimensional continuous wavelet transform
(CWT)
of a time signal, 86
Daubechies (dbX) wavelets, 87–88
inverse continuous wavelet transform
(ICWT), 87
Mexican hat mother wavelet, 87
other mother wavelets, 88
One-dimensional discrete Fourier transform
(1-D DFT), see Discrete Fourier
transform (DFT)
One-dimensional discrete wavelet transform
(1-D DWT)
definition, 88–89
on discrete signals, 90–94
synthesis equation, 89
One-dimensional (1-D) signal, 3
Optical microscopy
design, 356
disadvantages, 357
geometry, 356
limitation, 356–357
Rayleigh criterion, 357
representative image, 357–358
resolution, 357
P
Parkinson’s disease, 209
Perceptrons, 140–144
PET, see Positron emission tomography (PET)
Petit mal, 207
Point detection methods, 70–71
Point processing
bit-level slicing, 43–44
contrast enhancement, 41–43
definition, 40
histogram equalization, 44–47
Positron emission tomography (PET)
applications