Medical Image Processing with IDL. This is a three-day course on image processing in IDL, with an emphasis on hands-on demonstration of image processing

Medical Image Medical Image Processing Processing

with IDLwith IDL

Medical Image Processing with IDLMedical Image Processing with IDL

This is a three-day course on image processing in IDL, with an emphasis on hands-on demonstration of image processing techniques commonly used in medical research.

Central to the course is an explanation of how to use IDL’s DICOM toolkit to read, write, copy and query DICOM format files, through several examples and exercises.

Participants should be familiar with the IDL programming language, having completed RSI’s Introduction to IDL course, or having at least 3 months experience using IDL.

Course ObjectivesCourse Objectives

1. IDL and the DICOM standard (Day 1)– Overview of the DICOM standard– Introduction to IDL's DICOM toolkit– Examples of querying, reading, writing and cloning DICOM

files with IDL

2. Image processing in IDL (Day 2-3)– Diagnostic and statistical routines– Pixel operations– Neighborhood operations– Global transforms– Geometric operations– Feature extraction

Part I: IDL and the DICOM StandardPart I: IDL and the DICOM Standard

The DICOM StandardThe DICOM Standard

DICOM (Digital Imaging and Communications in Medicine) was developed in response to the growing need for a standard format for digital imagery produced by instruments from various vendors.

The DICOM standard is intended to– promote communication of digital imagery, regardless of

device manufacturer– facilitate development of PACS that can interface with other

hospital systems– allow creation of databases that can be interrogated by a

wide variety of devices distributed geographically.

The DICOM standard is currently in version 3. It is documented in 17 "parts", which can be downloaded from the DICOM homepage: http://medical.nema.org.


Part 1: Introduction and OverviewPart 2: ConformancePart 3: Information Object Definitions Part 4: Service Class Specifications Part 5: Data Structure and Semantics Part 6: Data Dictionary Part 7: Message Exchange Part 8: Network Communication Support for Message ExchangePart 9: Point to Point Communication Support for Message ExchangePart 10: Media Storage and File Format for Media Interchange Part 11: Media Storage Application Profiles Part 12: Media Formats and Physical Media for Media Interchange Part 14: Grayscale Standard Display Function Part 15: Security and System Management Profiles Part 16: Content Mapping ResourcePart 17: Explanatory InformationPart 18: Web Access to DICOM Persistent Objects


Diagnostic imaging modalities using DICOM today include– NM - Nuclear Medicine– PET - Positron Emission Tomography– MR - Magnetic Resonance– XR - X-Ray– US - Ultrasound– CT - Computed Tomography– RT - Radiation Therapy– CR - Computed Radiography

DICOM FilesDICOM Files

DICOMHeader

patient info, modality,

image size,etc. Header

Contains information, organized in attributes or data elements, describing the contents of the file.

DataContains image pixel data, the structure and encoding of which is described in the header. The file can contain multiple frames as well as compressed data.

A file conforming to DICOM standard PS 3.10 is known as a DICOM file. DICOM files have two components:

DICOM File Header StructureDICOM File Header Structure

An attribute or data element is comprised of four fields:

• Tag - a unique numeric label that identifies the attribute• Value representation (VR) - a two-character code describing the

type and format of the attribute's value• Value length - the length of the attribute's data• Value field - the attribute's data

Tag VRValueLength

Value Field

DICOM File Header StructureDICOM File Header Structure

• Attribute tags are expressed in the form

(gggg, eeee)

where gggg is the four-digit group and eeee is the four-digit element number, both in hexadecimal notation.

• Even-valued groups are public, defined by the DICOM standard; odd-valued groups are private, defined by users of the format. Examples:

– group 0008 : file creation information– group 0010 : patient information– group 0018 : imaging device parameters

• A full list of attribute tags and their VRs are given in DICOM PS 3.6, the Data Dictionary. A subset of these tags are listed in the Medical Imaging in IDL reference document.

DICOM File Header Structure: ExampleDICOM File Header Structure: Example

Tag VR

Value Length

Description Value Field

(0010,0000) UL 1 Group 0010 Length 62

(0010,0010) PN 8 Patient's Name Doe John

(0010,0020) LO 11 Patient's ID 123-45-6789

(0010,0030) DA 8 Patient's Birth Date 19360513

(0010,0040) CS 1 Patient's Sex M

DICOM File: C:\RSI\IDL62\examples\data\mr_abdomen.dcm

The IDL DICOM ToolkitThe IDL DICOM Toolkit

The IDL DICOM toolkit consists of two interfaces to DICOM format files:

IDLffDICOM IDLffDICOMex

These interfaces– conform to DICOM standard PS 3.10 for reading DICOM

files; IDLffDICOMex can also write DICOM files– employ an object-oriented programming methodology– can be used at the command line or within an IDL program– are fully documented, with examples, in IDL's Online Help

system


IDLffDICOM

• provides basic read access to a DICOM file, including– group/element tag– value representation– length– data values– file header preamble– data dictionary description for individual elements– embedded sequences of elements

• is included in the standard IDL distribution• has procedural wrappers QUERY_DICOM and READ_DICOM


IDLffDICOMex

• can query, read, clone, or create new DICOM files• can read and write both public and private attributes including

sequences and sets of repeating tags within sequences• can read and write compressed DICOM files on Windows and

UNIX-based platforms• can copy DICOM attributes from one file to another• supports JPEG and JPEG2000 files (except Mac OS X)• is built on an industry-standard commercial DICOM library from

Merge Technologies, Inc. (www.merge.com)• requires a special IDL license

IDL Variable Type: ObjectIDL Variable Type: Object

IDL's DICOM toolkit is implemented using object technology, a programming methodology used, for example, in java and C++

Terminology:

class - an abstract data type, where methods and properties are defined

object - an instance of a class

method - a program defining what an object can do

property - a datum of an object

Example: IDL is a programming language.

IDL Variable Type: ObjectIDL Variable Type: Object

An IDL object consists of a reference + a heap variable; the heap

variable is persistent in memory.

Creation

Use

Destruction

file = 'C:\MIPI\images\PET_1.dcm'odcm = obj_new('idlffdicomex', file)

odcm->getproperty, modality=m

obj_destroy, odcm

class nameobject reference

method invocation operator

method property

object_lifecycle.pro

IDL Variable Type: PointerIDL Variable Type: Pointer

Pointers are a persistent data type similar to objects, consisting of a

reference and a heap variable.

IDL pointers are unlike C pointers in that they do not directly access

memory.

Creation

Use

Destruction

pointer_lifecycle.pro

p = ptr_new('My dog has fleas.')

print, *p

ptr_free, p

pointer reference

pointer dereference operator

The IDLffDICOM ClassThe IDLffDICOM Class

• Introduced in IDL 5.2 (1998) to support the growing use of the DICOM standard.

• API

• Examples

1. dumping header tags to screen or file

2. retrieving header tag descriptions, VRs and values

3. reading image pixel data

4. data visualization (TV & TVSCL, IIMAGE, IVOLUME)• Procedural wrappers

odcm = obj_new('idlffdicom')

The IDLffDICOM Class: Example 1The IDLffDICOM Class: Example 1

Read a DICOM file and dump its attributes to a text file.

View the dump file CT_1_hdr.txt.

; Make a new DICOM file object.odcm = obj_new('idlffdicom')

; Read the contents of a DICOM file.file = 'C:\MIPI\images\CT_1.dcm'info = odcm->read(file)

; Dump its attributes to a text file.odcm->dumpelements, 'CT_1_hdr.txt'

idlffdicom_ex1.pro


Extract one attribute's data from a DICOM file header.

ref = odcm->getreference('0008'x,'0060'x)vr = odcm->getvr('0008'x,'0060'x)vl = odcm->getlength('0008'x,'0060'x)dsc = odcm->getdescription('0008'x,'0060'x)val = odcm->getvalue('0008'x,'0060'x)

; Print attribute's description and value.print, dsc, *val[0]

idlffdicom_ex2.pro

The attribute referenced by (0008,0060) lists the modality of the device used to create the image.


Read image pixel data and attributes for displaying the data.

; Get display attributes.n_images = odcm->getvalue('0028'x,'0008'x)xsize = odcm->getvalue('0028'x,'0011'x)ysize = odcm->getvalue('0028'x,'0010'x)bit_depth = odcm->getvalue('0028'x,'0101'x)

; Get the image pixel data.image = odcm->getvalue('7fe0'x,'0010'x)

idlffdicom_ex3.pro

Group 0028 gives information about the image, group 7fe0 gives the image pixel data.


tv, *image[0], /ordertvscl, *image[0], /orderiimage, *image[0], /orderivolume, image_stack, /order

idlffdicom_ex4a.proidlffdicom_ex4b.pro

The iTools can also be used for simple image processing; more on that later in the course.

Display image pixel data from a DICOM file. Image data can be displayed in IDL with

• TV / TVSCL• IIMAGE• IVOLUME

The IDLffDICOM ClassThe IDLffDICOM Class

The IDLffDICOM class has two procedural wrappers.

QUERY_DICOM - tests that the file is a DICOM file, optionally returning a structure of information

READ_DICOM - reads the image pixel data from a DICOM file

The wrappers are written in the IDL language.

idlffdicom_wrappers.pro

ok = query_dicom(dicom_file, info)

image = read_dicom(dicom_file)

The IDLffDICOM Class: ProblemsThe IDLffDICOM Class: Problems

1. What is the patient's name in CT_2.dcm in the course files? What is the referring physician's name?

2. Read the image pixel data from mr_brain.dcm in the examples/data subdirectory of the IDL distribution. Display the data with TV in a window sized for the image.

3. Write a program to read the first four images from mr_abdomen.dcm (also in examples/data) and display them in a 2x2 grid in IIMAGE.

The IDLffDICOMex ClassThe IDLffDICOMex Class

• Introduced in IDL 6.1 (2004) to upgrade IDL's ability to work with DICOM, especially the ability to clone and write DICOM files.

• API

• Examples– read the attributes into an array of IDL structures– retrieve and display image pixel data– query the attributes of a DICOM file– clone a DICOM file and alter its attributes– create a new DICOM file

• The DICOM_VIEWER demo application

odcm = obj_new('idlffdicomex', file)

The IDLffDICOMex Class: Example 1The IDLffDICOMex Class: Example 1

Read the attributes of a DICOM file into an array of IDL structures.

; File is opened read-only, by default.file = 'C:\MIPI\images\US_10.dcm'odcm = obj_new('idlffdicomex', file)

; Dump attributes to a structure array.tags = odcm->enumeratetags()

; Dump attributes to a file.tags = odcm->enumeratetags( $

filename='US_10_hdr.txt')

idlffdicomex_ex1.pro

All attributes are read by default, though a subset can also bespecified.


Read image pixel data and display with IIMAGE.

; Display image pixel data with IIMAGE.iimage, odcm->getpixeldata()


Note that the GetPixelData method automatically flips the image data; setting the ORDER keyword to IIMAGE (or TVor TVSCL) is not necessary.


Check that an attribute exists before attempting to read it.

; Is the transfer syntax attribute present?is_present = odcm->queryvalue('0002,0010')

if is_present eq 2 then begin; Use GetProperty or GetValue to; retrieve the transfer syntax.odcm->getproperty, transfer_syntax=tsts1 = odcm->getvalue('0002,0010')

endif

idlffdicomex_ex3a.proidlffdicomex_ex3b.pro

Use QueryValue to test for an attribute's existence. An error results from attempting to access an attribute that doesn't exist.


Clone a DICOM file.

old_file = 'C:\MIPI\images\CR_1.dcm'new_file = 'C:\MIPI\images\MIPI_CR_1.dcm'odcm = obj_new('idlffdicomex', new_file, $

clone=old_file)

; Change the image type, then commit change.type = ['ORIGINAL','SECONDARY']odcm->setproperty, image_type=typeodcm->commit


Original DICOM files (i.e., directly from a scanner) should not be modified. Instead, the file should be cloned and labeled as such.


Create a new DICOM file.

; Create a new DICOM file, specifying ; its type with the SOP_CLASS keyword.; Commit the changes.file = 'C:\MIPI\images\new.dcm'odcm = obj_new('idlffdicomex', file, $

/create, /non_conforming, $sop_class='STANDARD_NM')

odcm->commit


The IDLffDICOMex Class: ProblemsThe IDLffDICOMex Class: Problems

1. Read only the tags from group (0002) in the clone file MIPI_CR_1.img. Write the tags to a text file. Compare them with the elements of the same group in CR_1.dcm.

2. Write a program to read the image pixel data from mr_brain.dcm (in the examples/data subdirectory of the IDL distribution) and display them with TV in a window sized for the image. Compare with problem #2 for IDLffDICOM.

3. Clone CR_1.dcm and reduce the size of the file by selecting a transfer syntax that supports compression.

4. Read the file ctscan.dat from the examples/data subdirectory and create a new (nonconforming) DICOM file with its image pixel data.

Further InformationFurther Information

For more information on IDL'sDICOM capabilites, see

• http://www.rsinc.com/dicom– Toolkit information– Conformance statement

• http://www.rsinc.com/webinar– "IDL Medical Imaging

Suite" presentation

• Medical Imaging in IDL in the IDL Online Help

Part II: Image Processing in IDLPart II: Image Processing in IDL

Images and Image ProcessingImages and Image Processing

negation

input operation output

An image is a discrete, two-dimensional signal representing a physical phenomenon.

Image processing is the body of methods for extracting information

from images, typically expressed as a functional relationship

between input and output images.

OutlineOutline

1. IDL arrays and diagnostic/statistical routines2. Pixel operations

• stretching/scaling, window center and width, thresholding, masking, negation, mathematical operations, equalization

3. Neighborhood operations• smoothing, median filtering, convolution, sharpening, edge

detection4. Global transforms

• Fourier and Radon transforms5. Geometric operations

• rotating, scaling and resizing images; registration, fusion6. Feature extraction methods

• regions of interest (ROI), region growing, morphological operators, segmentation

IDL Arrays IDL Arrays and Diagnostic / Statistical Routinesand Diagnostic / Statistical Routines

IDL ArraysIDL Arrays

• IDL is an array-based language.• Images, image stacks and cines are represented in IDL as 2-, 3-

or 4-D arrays, consisting of– 1-, 2-, 4- or 8-byte unsigned integers– 2-, 4- or 8-byte signed integers– 4- or 8-byte floating point values

• Array operations are faster than scalar operations.– example: find maximum pixel value of image

• IDL has many built-in, array-oriented routines for image processing (see Online Help).

array_operation_ex.pro

Diagnostic and Statistical RoutinesDiagnostic and Statistical Routines

A list of routines used to get information about an image.

HELP SIZE MIN

MAX WHERE ARRAY_INDICES

MOMENT MEDIAN ARRAY_EQUAL

TOTAL PRODUCT IMAGE_STATISTICS

FINITE HISTOGRAM N_ELEMENTS

UNIQ SORT

helpful_routines_ex.pro

HistogramHistogram

The histogram is the graph of the frequency distribution of an image's pixel values. Integrate the histogram over pixel value to obtain thecumulative histogram.

image

histogram

histogram_ex.pro

h = histogram(image, locations=x)plot, x, h

c = total(h, /cumulative)plot, x, c

ProblemsProblems

1. Write a function that returns the range of values in an array.

2. Write a function that returns the odd values of an input array. (Hint: use WHERE)

Pixel OperationsPixel Operations

Pixel OperationsPixel Operations

• Given an input image A, a pixel operator F yields an image B where the value of B at a particular pixel (x,y) depends only on the same pixel in A.

• Operations– stretching / scaling– window center and width– mathematical operations– thresholding and masking– histogram equalization

A

B

Stretching / ScalingStretching / Scaling

Stretching or scaling enhances the contrast inan image by broadening a selected region of theimage's histogram.

The IDL minimum operator <returns the smaller of its twooperands, the maximumoperator > returns the larger.Both are array operators.

scaling_ex.pro

scaled_image = (image > lo) < hi

original

scaled

Window Center and WidthWindow Center and Width

The window center and width define an intensityrange into which the pixels of an image are scaled.

These quantities are commonly known as thebrightness and contrast of the image.

lo = center - width/2hi = center + width/2scaled_image = (image > lo) < hi

window_settings_ex.pro

original

scaled

Arithmetic OperationsArithmetic Operations

Arithmetic operators, which are array operatorsin IDL, can be applied directly to the pixel valuesof an image.

In this example, the range ofimage pixel intensities is halved, decreasing the separation of the fine detailsof the image.

image *= 0.5

arithmetic_ex.pro

original

scaled

ThresholdingThresholding

Thresholding is a simple form of segmentation.Pixels that satisfy a relational operation are set

to1, the rest are set to 0, giving a binary image.

Thresholding is typically usedto separate an image's fore-ground and background features. Here, attempt to separate the bone from the soft tissue in this CT image.

binary_image = image ge threshold

original

binary result

thresholding_ex.pro

MaskingMasking

Like thresholding, image masks are created with rela-tional operators. When applied, a mask blocks pixelsbeneath it, allowing the remaining pixels to pass.

This example uses the WHERE function to identifypixels associated with boneand set those pixels to a newvalue, identified with a yellow color.

i_mask = where(image ge threshold)masked_image[i_mask] = max(image)

original

masked image

masking_ex.pro

Histogram EqualizationHistogram Equalization

Histogram equalization increases the contrast in an image byremapping its pixel values, spreading the image's histogram over the full range of intensity values.

eq_image = hist_equal(image)

equalization_ex.pro

original image histogram equalized histogram equalized image

ProblemsProblems

1. Make an IDL function to set the window center and width for an image.

2. Construct a mask to remove a square region of 100x100 pixels from the center of the image kidney20.dcm.

3. Create a new image from kidney20.dcm where the black pixels around the edge are transparent.

Neighborhood OperationsNeighborhood Operations

Neighborhood OperationsNeighborhood Operations

• Given an input image A, a neighborhood operator F yields an image B where the value of B at a particular pixel (x,y) depends on pixels in the neighborhood of the same pixel in A.

• Operations– smoothing– median filtering– convolution– sharpening– edge enhancement

A

B

SmoothingSmoothing

Smoothing removes high-frequency informationfrom an image. Smoothing is typically used toattenuate noise or soften an image.

The SMOOTH function is used toapply a rectangular running-mean(or tophat) filter to an image.

smoothing_ex.pro

smoothed_image = smooth(image, 5)

original

filtered imagesmoothing

kernel

Median FilteringMedian Filtering

Median filtering replaces each point in an imagewith the median intensity value of a two-dimensional neighborhood of a given width.

Median filtering removes outlying values from animage without altering or introducing new pixelsinto the image, thereby preserving edges in theimage.

median_filtering_ex.pro

smoothed_image = median(image, 5)

original

filtered image

ConvolutionConvolution

Convolution is the process of mathematically

blending a kernel with an image. The choice of

kernel determines the nature of the output image.

Here, a Laplacian kernel is used

to differentiate the input image. In

the resulting image, higher pixel

intensities denote regions of rapid

change.

convolution_ex.pro

kernel = intarr(3,3) - 1kernel[1,1] = 8d_image = convol(image, kernel, 9)

original

differentiatedimage

Laplaciankernel

SharpeningSharpening

Sharpening enhances contrast by boosting thehigh-frequency components of an image.

Many techniques exist.Here, a highpass filteredimage is added to the original image. TheLaplacian kernel from the previous slide is used.

sharpening_ex.pro

original

sharpened image

hipass = convol(image, kernel, 9)sharp = image + hipass

Edge EnhancementEdge Enhancement

Edge enhancement routines use differentiating kernels to increasethe brightness of edges in an image. Different kernels can be usedto enhance different edges in the image.

r_image = roberts(image)s_image = sobel(image)

original image with Sobel filter with Roberts filterwith horizontaldifference filter

edge_enhancement_ex.pro

ProblemsProblems

1. Write a program to display the difference between images filtered with the SMOOTH and MEDIAN functions.

2. Use CONVOL to apply a Gaussian smoothing kernel to an image. Compare the result with output from SMOOTH for the same kernel size.

3. Demonstrate that prior smoothing of an image can improve the results of edge enhancement routines.

Global TransformsGlobal Transforms

Global TransformsGlobal Transforms

• Given an input image A, a global transform F yields an image B where the value of B at a particular pixel (x,y) depends on all the pixels in A.

• Operations– Fast Fourier transform– Radon transform

A

B

Fast Fourier TransformFast Fourier Transform

The fast Fourier transform (FFT) is an algorithm for transformingdiscrete data between the physical and frequency domains.

IDL's FFT can operate on arrays of up to 8 dimensions; the FFT isperformed on each dimension separately the same routine isused for all image and cine data.

; Forward transform.image_hat = fft(image)

; Inverse transform.image = fft(image_hat, /inverse)

Fourier

transform

physical domain

frequency domain

forward in

vers

e

Fast Fourier Transform: SpectrumFast Fourier Transform: Spectrum

The power spectrum is a graph of the relative frequency contrib-utions to the total variance of an array represented in the frequency domain.

; Power spectrum.power = abs(image_hat)^2

imageshifted powerspectrum - log

shiftedpower spectrum

powerspectrum

spectrum_ex.pro

Fast Fourier Transform: FilteringFast Fourier Transform: Filtering

Filtering is used to alter an image by attenuating, isolating or removing features of the image in the frequency domain.

filte

rso

pera

tion

s lowpass bandstopbandpasshighpass

exponentialButterworthideal

filters_and_operations_ex.pro

Fast Fourier Transform: ExampleFast Fourier Transform: Example

Lowpass filter an image using an ideal filter with a cutoff at 1/4 theNyquist frequency for the image.

image_hat = fft(image)cutoff = (xsize < ysize) / 8filter = dist(xsize,ysize) le cutoffimage_lowpass = fft(image_hat*filter, /inverse)

image filtered imagefiltered

power spectrumpower spectrum

lowpass_ex.pro

Radon TransformRadon Transform

The Radon transform is used in reconstructing imagery from CTscanners. Here, a mask is constructed to brighten edges in an image.

radon_ex2.pro

; Forward transform.image_hat = radon(image, rho=rho, theta=theta)

; Inverse transform.image = radon(image_hat, /backproject, $

rho=rho, theta=theta)

image contrastbackprojectionedge-enhanced Radon transform

ProblemsProblems

1. Sharpen an image using Fourier filtering. Compare the results with those from SHARPENING_EX in the previous section.

Geometric OperationsGeometric Operations

Geometric OperationsGeometric Operations

• A geometric operator F maps the pixels of an image A into a new coordinate system to obtain the image B.

• Operations– manipulating (rotating, cropping, etc.) images– registration– image fusion A

B

Manipulating ImagesManipulating Images

IDL provides built-in array operators and array manipulationroutines that can be used to transform an image's geometry.

Examples:• Cropping• Padding• Extracting image planes• Rotating/Flipping

– ROTATE, SHIFT, TRANSPOSE• Resizing

– REBIN, CONGRID

manipulation_ex.pro

RegistrationRegistration

Registration is a procedure for determining the best spatial fit fortwo or more images that overlap a scene.

Methods:• cross-correlation• control points

registration_ex1.proregistration_ex2.pro

image 2image 1

calcium map

Image FusionImage Fusion

Image fusion is a procedure for melding images from differentmodalities that overlap the same scene.

IDL application: the IDLmedFusion class fuses two images, usingone of 23 blending functions, producing an indexed or an RGBimage as a result.

idlmedfusion__define.pro fusion_image_demo.pro

ProblemsProblems

1. Crop the text from US_10.dcm in the MIPI image directory.

Feature Extraction MethodsFeature Extraction Methods

Feature Extraction MethodsFeature Extraction Methods

• Given an input image A, a feature extraction operator F selects, emphasizes, or modifies certain aspects of image A.

• Operations– regions of interest (ROIs), region growing– morphological operations– segmentation

A

B

Regions of Interest (ROI)Regions of Interest (ROI)

A region of interest (ROI) is a subset of an image marked for further analysis.

In IDL, an ROI is defined pointwise by a set of verticesenclosing the subset.

IDL has built-in tools for defining ROIs both interactively and algorithmically.


• Manual or interactive ROI definition:– XROI– IIMAGE

• Automatic or algorithmic ROI definition:– CONTOUR– SEARCH2D / SEARCH3D– REGION_GROW

• Quantification:– IDLanROI class– IMAGE_STATISTICS


• An object of the IDLanROI class represents the set of vertices defining an ROI.

• API

• Used for– computing statistics– adding/removing points– generating a binary mask– rotating, translating or scaling the ROI– checking whether a point is interior/exterior to the ROI

oroi = obj_new('idlanroi')

Regions of Interest (ROI) : Example 1Regions of Interest (ROI) : Example 1

Interactively define an ROI with XROI, then compute statistics and

define a mask with IDLanROI.

xroi_ex.pro

Regions of Interest (ROI): Example 2Regions of Interest (ROI): Example 2

Perform a flood fill analysis with

SEARCH2D, automatically defining

a region.

Recover the bounding points of the region

with CONTOUR, obtaining the vertices

of an ROI.

Compute ROI statistics; construct an

image mask.

search2d_ex.pro

Regions of Interest (ROI): Example 3Regions of Interest (ROI): Example 3

Region growing is a process by which an initial region is expanded

to include neighboring "like" pixels, based on certain limits.

region_grow_ex.pro

new_region = region_grow(img, region, $threshold=[215,255])

initial region (red) threshold method multiplier method

Morphological OperationsMorphological Operations

Morphological operators emphasize shapes in binary (b) or grayscale (g) images, based on the geometry of a structuringkernel.

Library routines:• ERODE [b,g]

• DILATE [b,g]

• MORPH_CLOSE [b,g]

• MORPH_OPEN [b,g]

• MORPH_GRADIENT [g]

• MORPH_DISTANCE [b]

• MORPH_HITORMISS [b]

• MORPH_THIN [b]

• MORPH_TOPHAT [g]

• WATERSHED [g]

hit

miss

MORPH_HITORMISS

Morphological Operations: Example 1Morphological Operations: Example 1

The morphological closing operation fills small gaps in a binary or

grayscale image.

morph_close_ex.pro

closethreshold

bmoz = moz le 250Bkernel = replicate(1,3,3)bmoz = morph_close(bmoz, kernel)

Morphological Operations: Example 2Morphological Operations: Example 2

The morphological gradient operation highlights edges of objects

in a grayscale image.

morph_gradient_ex.pro

gradient + thresholdgradient

r = 2 disc = shift(dist(2*r+1), r, r) le rgrad = morph_gradient(img, disc)

original

SegmentationSegmentation

Image segmentation is the process of subdividing an image into

distinct regions, based upon some set of criteria.

In IDL, thresholding and the morphological operators can be used

for segmentation.

Segmentation: ExampleSegmentation: Example

How many cells are in this image?

It is possible to count the cells by

eye, but if hundreds of images

were to be processed, automation

would be more efficient.

LABEL_REGION can be used to index the

autonomous regions of a binary image.

label_region_ex.pro

regions = label_region(bimage)

ProblemsProblems

1. Define ROIs from the two regions created in the example program REGION_GROW_EX.

2. Experiment with altering the geometry of the structuring elements in the MORPH_* examples. How sensitive are the results to the size and shape of the structuring elements?

3. Use LABEL_REGION to count the cells in rbcells.jpg in the examples/data subdirectory.

ReferencesReferences

DICOM information• Official NEMA site http://medical.nema.org• Prof. Chris Rorden

http://www.psychology.nottingham.ac.uk/staff/cr1/dicom.html• Dr. David Clunie

http://www.dclunie.com/medical-image-faq/html/toc.html

Image processing• Easton, R. Fundamentals of Digital Image Processing. Short

course, Rochester Institute of Technology, July 1988.• Gonzales, R.C. and R.E. Woods. Digital Image Processing.

Reading, Massachusetts: Addison-Wesley, 1992.• Russ, J.C. The Image Processing Handbook. Second edition.

Boca Raton, Florida: CRC Press, 1995.

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Documents

Medical Image Processing with IDL. This is a three-day course on image processing in IDL, with an emphasis on hands-on demonstration of image processing