image processing and analysis i materials extracted from gonzalez & wood and castleman
TRANSCRIPT
Image Processing and Analysis I
Materials extracted from Gonzalez & Woodand Castleman
Light source
Detector
Intermediate Optics
Specimen
A typical biomedical optics experiment
Image Processing
Image Data BaseKnowledge Base
Digital Image ProcessingA process to extract information from image data
Image Acquisition
Image Annotation,
Compression,Storage/Retrieval
Preprocessing Segmentation Representation Recognition
Interpretation,Modeling
ProblemDefinition Results
Display,Animation
Materials extracted fromReferences texts:
Gonzalez & Woods, and
Castleman
Image Processing Example 1 – Pap Smear
Benign Squamous Cells Squamous Cell Carcinoma
One of the few histopathological tasks where image recognition system is becoming commercial
Image Processing Example 2 – Breast X-Ray
The distinction between benign and malignant can be difficult for breast x-rayRadiologist are highly trained in image recognition
Most biomedical imaging today does not address underlying molecular and cellular based mechanisms
Image Data Base – Format and Data Base
Header
Hyper-Data
Data
Monochrome Color
1 bit – binary
8 bit
16 bit
32 bit
32 bits (float)
RGBR (8 bit)G (8 bit)B (8 bit)
Gray
Scale
Image Preprocessing – histogram and contrast
Image Preprocessing – histogram equalization
Let r be the gray level value of a pixel in the image.
]1,0[r ; Map each gray level value r to a new value s: )(rTs The histogram distribution of the original image is: )(rPr
The histogram distribution of the new image is: )(sPs
In general: )(1])([)(sTrrs ds
drrpsP
Histogram equalization is defined as the transform: r
r dwwprTs0
)()(
Since )(rPdr
dsr , 1)( sPs for histogram equalization
Image Preprocessing – histogram and contrast
Convolution and Image Processing
Recall the definition of convolution:
dthgthtg )()()()(
Graphical explanation of convolution:
Convolution Theorem
)(~
)(~))(( fhfgfhg
dtedthgdtethg ftifti 22 )()()(
)(22 )()( tfifi etdthegd
)'(22 )'(')( tfifi ethdtegd
)(~
)(~ fhfg
where dtdttt ''
Image Preprocessing – Noise Reduction, averaging, low pass filter, median filter
1
1 M
M
Averaging M images reducenoise by sqrt(M)
Low Pass Filter
3x3 kernel 5x5 kernel
111
111
111
9
1
Median Filter
Replace center pixel value by the median value
from a nxn pixel neighorhood
11111
11111
11111
11111
11111
25
1
Avg = 30; Median =10
200108
1296
10127
Avg by 2,8,16,32,128 Kernel 3,5,7,15,25 5x5 low pass vs median