mipr lecture 5 copyright oleh tretiak, 2004 1 medical imaging and pattern recognition lecture 5...

MIPR Lecture 5Copyright Oleh Tretiak, 2004

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Medical Imaging and Pattern Recognition

Lecture 5 Image Measurements and

OperationsOleh Tretiak


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Previous Lectures

• Images represent objects. We wish to see or measure items in images.

• Image quality is affected by resolution and sampling.

• Image quality is affected by noise.• Images are stored in files, and are

displayed with pixels. They have grey scale.


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This Lecture

• Image enhancement– Seeing more in images

• Image measurement– Data from images


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Image Enhancement

• Contrast operations• Image scaling, rotation• Filtering

– Noise reduction– Edge enhancement

• Image subtraction• Image addition• Image stacks


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Contrast Operations

• I(x,y) - initial image; O(x,y) - output image;• At each x,y point:

O(x, y) = cI(x,y)+b

c ~ contrast, b ~ brightness

I(x, y) O(x, y)


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Histogram

• I(x, y) image, 0≤I(x,y)≤k integer values only• H(j), 0≤j≤k histogram of image I. • H(j) = number of pixels for which I(x, y) = j


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Contrast Control Features

ImageJ Brightness & Contrast Control

“Historgram Equalization”An automatic contrast adjustment method.


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Image Scaling, Rotation, Cropping

Original, 128x128

Rotate by 15°

Scale by 2,256x256

Scale by 0.5, 64x64,less information.


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What is Image Filtering?

• Compute new image from old image• Each pixel in new image depends on ‘nearby’ pixels in

old image• For all x, y

O(x, y) = F(I(x, y), I(x+1, y), I(x-1, y), I(x, y+1), I(x, y-1))

x

y

x

y

I(x, y) O(x, y)


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Examples-Formulas

• Examples – O(x, y) = {I(x, y)+I(x+1, y)+I(x-1, y)+I(x, y+1)+I(x, y-1)}/5

• local average– O(x, y) = {4I(x, y)-I(x+1, y)-I(x-1, y)-I(x, y+1)-I(x, y-1)}

• Laplacian– O(x, y) = {I2(x, y)+I2(x+1, y)+I2(x-1, y)+I2(x, y+1)

+I2(x, y-1)}/5• Quadratic (nonlinear)

• Output depends on neighboring pixels of the input function

• Neighborhood can be small or large


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Linear Low-Pass Filter

• Use: Noise reduction– ImageJ function Process->Smooth


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Median Filter

• Use: Noise reduction– ImageJ: Process->Filters->Median...


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Find Edges

• ImageJ: Process->Find Edges


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Image Operations

• Unary operations (Process->Math->...)– O(x, y) = log(I(x, y))– O(x, y) = 1/I(x, y)

• Binary operations with constant (Process->Math->...)– O(x, y) = I(x, y) + a– O(x, y) = I(x, y)*b

• Binary operations with images (Process->Image Calculator)– O(x, y) = I1(x, y) + I2(x, y)– O(x, y) = I1(x, y) – I2(x, y)


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Example: Image Subtraction

Slice 22 Slice 23 22 - 23


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Stacks

• Stacks are sequences of images, all have the same dimensions

• Most often, a stack is a sequence of slices through a 3-D object

• May be a time sequence (movie)


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Example: Stack Converted to Montage


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Image Measurements

• Types of Measurement– Geometry– Intensity

• ROI (region of interest)• Segmentation


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Why Measure?

• Numbers are inputs to decisions• Example of geometric

measurement: Area• Example of intensity

measurement: amount of DNA


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Measurements in ImageJ

• Area• Bounding

Rectangle• Circularity• Centroid• Perimeter• Fit Ellipse• Feret’s Diameter

• Grey value standard deviation

• Min & Max Grey Value

• Center of Mass• Mean Gray Value• Modal Gray Value


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Example of Measurements

• Area 16384• Mean 158.431• StdDev 57.680• Mode 194• Min 0• Max 255• X 64• Y 64• ...

128x128


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How to Measure Head Only?

• Specify region of interest (ROI)• Measure within ROI• Method of specifying ROI

– Manual– Automatic– Combination


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Manual Selection Tools

• Rectangle• Oval• Polygon• Freehand

QuickTime™ and aTIFF (Uncompressed) decompressor

are needed to see this picture.

Oval selection tool. Area 5626, Mean 97.139, ....


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Semi-Automatic Selection

• Use Image->Adjust->Set Threshold to specify gray value to be measured

• Use magic wand to select objects• Measure the objects

Image with threshold settingQuickTime™ and a

TIFF (Uncompressed) decompressorare needed to see this picture.

QuickTime™ and aTIFF (Uncompressed) decompressorare needed to see this picture.

Selection madewith magic wand


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Analyze Particles Command

• Find a point in image using specified threshold

• Trace outline with magic wand• Measure interior• Clear selection• Continue until done


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Example

Images have been analyzed. 27 sets of measurements have been recorded.


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Image Processing Methods

• Image enhancement• Image measurement

– Image enhancement can be a preliminary step to measurement

• Important image enhancement– Contrast adjustment– Image scaling


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Image Measurements

• Specify ROI (region of interest, selection)

• Measure– Geometric features– Gray level features

• Manual ROI is reliable• Automatic ROI is sometimes

possible

mipr lecture 5 copyright oleh tretiak, 2004 1 medical imaging and pattern recognition lecture 5...

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