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Image analysis
Daniel Smutek
Biological imaging
• Radiology (clinical imaging)
• Endoscopy
• Microscopy
• Maps from biomedical signals
– electroencephalography (EEG)
– magnetoencephalography (MEG)
Clinical/Medical Imaging
• X-ray (Projection radiography)
-contrast, double contrast
• Computed tomography (CT)
• Ultrasound
• Magnetic resonance imaging (MRI)
• Nuclear medicine
– Positron emission tomography (PET)
Invasive/Non-invasive
• X-ray
• CT
• Nuclear Imaging
• Ultrasound
• MRI
vs contrast agents(gadolinium-based, microbubbles)
radiation exposure, adverse
reactions to contrast agents
(barium sulfate vs iodine agents)
Radiation exposure
ExaminationRelative
dose
Chest X-ray 1
Head CT 75
Abdomen CT 265
Chest CT 290
Chest, Abdomen and Pelvis CT 495
Cardiac CT angiogram 500
CT colonography (virtual colonoscopy) 250
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Digitalization:
• Sampling – Aividing the image in pixels. Sampling gives us resolution of a digital image (e.g. in megapixels –Mpx). It is necessary to choose an appropriate resolution to preserve all the information that we want to capture
• Quantization - Assigning a numerical value to every pixel. The value represents color (brightness) of the pixel. E.g. 8-bit quantization offers 28 = 256 numerical values for each pixel
• Binary reprezentation – coding the image in bites for writing into a data file
Resolution – given by samling256x256 pixels 128x128 pixels 64x64 pixels
32x32 pixels 16x16 pixels 8x8 pixels
No. shades of grey – given by quantization
256 shades 16 shades
8 shades 4 shades 2 shades - black and white only
Noise
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Brightness and contrast
• The brightness value is assigned to each pixel – Grayscale images – typically 1 byte for each px
which can represent 256 shades of gray
– Color images • Three channls – Red, Green, Blue
• Every chanell has its own range of values
• E.g. TrueColor images – 1 byte (256 values) for Red channel, 1 byte (256 values) for Green channel, 1 byte (256 values) for Blue channel. In total 3 bytes = 24 bites, therefore Truecolor images provides 224 or 16,777,216 color variation
• Contrast - define as a brightness difference between two pixels
Image histogram• Graphical representation showing amount of pixels in the
image for each brightness value
• Useful when we want to determine if the image is too
dark (underexposed) or too bright (overexposed) or if it
has a small dynamic range (low overall contrast)
Native image
Decrease of
brightness
Increase of
brightness
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Změna jasuZvýšení jasu → přičtení konstanty >0
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Změna jasuSnížení jasu → odečtení konstanty >0
Native image
Decrease of
contrast
Increase of
contrast
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Změna kontrastuZvýšení kontrastu → násobení konstantou >1
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Změna kontrastuSnížení kontrastu → dělení konstantou >1
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Histogram Equalization - CT image
intelligent histogram adjustment that gives much
better contrast
Native image After equalization
Diagnosis
Computer Aided Diagnosis
(CAD)
Thyroid and its Function
Hyperfunction - orbitopathy
Hypofunction - cretenism
2D histogram – co-occurrence matrix
obraz – grey levels
kookurenční matice
(2D histogram) pro (1;0)
1D histogram
Haralick Texture Features for
2D Image Segmentation
co-occurrence matrix : normalization :
Haralick features :
We used 15 texture features:• entropy• texture contrast• texture correlation• texture homogeneity• inverse difference moment• ...
e.g. texture homogeneity :
DSA – digital subtraction angiography
-Subtraction
Contrast
adjustment
maskAfter injection
of contrast
medium
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Segmentation of CT 3D
Images in Medicine
• diagnosing
• organs segmentation
• Future-CAD project
Texture-based segmentation
of 3D Images
• 2D texture image segmentation well established
• 3D texture segmentation can use more information
Example – Results of Segmentation
• original image
• hand-made segmentation
• automatic segmentation using our method
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PACS
• Picture archiving and communication
system
• computers or networks dedicated to the
storage, retrieval, distribution and
presentation of images
• integration with
– hospital information system
– radiology information system (RIS).
DICOM
• Digital Imaging and Communications in Medicine
• Standard for medical imaging– handling
– storing
– printing
– transmitting
• file format definition
• network communications protocol (TCP/IP based)
DICOM format (Digital Imaging and
Communications in Medicine) - header
Image Type (0008,0008) 1-n CS [ORIGINAL\PRIMARY\M\NORM]
Study Date (0008,0020) 1 DA [2005.01.24]
Study Time (0008,0030) 1 TM [18:03:49.859000]
Modality (0008,0060) 1 CS [UZ]
Patient's Name (0010,0010) 1 PN [xxx]
Patient's ID (0010,0020) 1 LO [RC]
Slice Thickness (0018,0050) 1 DS [6]
Trigger Time (0018,1060) 1 DS [242.5]
Series Number (0020,0011) 1 IS [9]
Acquisition Number (0020,0012) 1 IS [1]
Instance (form...Image) Number (0020,0013) 1 IS [8]
Image Position (Patient) (0020,0032) 3 DS [-17.569595\-171.59116\114.26955]
Image Orientation (Patient) (0020,0037) 6 DS [0.77845185\0.62770433\-4.0211107e-009\-0.35008808\0.43416414\-0.830024]
Slice Location (0020,1041) 1 DS [-37.985614]
Rows (0028,0010) 1 US [256]
Columns (0028,0011) 1 US [192]
Pixel Spacing (0028,0030) 2 DS [1.40625\1.40625]
Bits Allocated (0028,0100) 1 US [16]
Bits Stored (0028,0101) 1 US [12]
High Bit (0028,0102) 1 US [11]
Pixel Data (7FE0,0010) 1 OW [49152 * 2 bytes at offset 23518]
Registration
Fin
d 9
heads
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Literature
• Špunda, Dušek & kol.: Zdravotnická informatika, Karolinum, Praha, 2007
• Kasal, Svačina: Lékařská informatika (Karolinum, Praha, 1998)
• Nekula & kol.: Radiologie, 3. vyd. (Univerzita Palackého, Olomouc, 2005)
• Urbánek: Nukleární medicína, 4. vyd. (Gentiana, Jilemnice, 2002)
• Huang: PACS and Imaging Informatics – Basic Principles and Applications, 1st edition (Wiley, New Jersey, 2004)
• Shortliffe, Cimino: Biomedical Informatics – Computer Applications in Health Care and Biomedicine, 3rd edition(Springer, New York, USA, 2006)
• Poznámky a prezentace z přednášek a seminářů, e-learningové materiály ze stránek ústavů 1. LF UK /2002 -2009/ (biofyzika, fyziologie, nukleární medicína, radiologie)
