Presentation on theme: "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."— Presentation transcript:
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 Objectives 1. 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. 2.Image processing in IDL (Day 2-3) – –Diagnostic and statistical routines – –Pixel operations – –Neighborhood operations – –Global transforms – –Geometric operations – –Feature extraction
The 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.
The DICOM Standard Part 1: Introduction and Overview Part 2: Conformance Part 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 Exchange Part 9: Point to Point Communication Support for Message Exchange Part 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 Resource Part 17: Explanatory Information Part 18: Web Access to DICOM Persistent Objects
The DICOM Standard 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 Files DICOM Header patient info, modality, image size, etc. Header Contains information, organized in attributes or data elements, describing the contents of the file. Data Contains 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 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 TagVR Value Length Value Field
DICOM 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: Example TagVR Value Length DescriptionValue Field (0010,0000)UL1Group 0010 Length62 (0010,0010)PN8Patient's NameDoe John (0010,0020)LO11Patient's ID123-45-6789 (0010,0030)DA8Patient's Birth Date19360513 (0010,0040)CS1Patient's SexM DICOM File: C:\RSI\IDL62\examples\data\mr_abdomen.dcm
The IDL DICOM Toolkit The IDL DICOM toolkit consists of two interfaces to DICOM format files: IDLffDICOMIDLffDICOMex 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
The IDL DICOM Toolkit 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
The IDL DICOM Toolkit 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: 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: 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 methodproperty object_lifecycle.pro
IDL 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 Class Introduced in IDL 5.2 (1998) to support the growing use of the DICOM standard. API Examples 1. 1.dumping header tags to screen or file 2. 2.retrieving header tag descriptions, VRs and values 3. 3.reading image pixel data 4. 4.data visualization (TV & TVSCL, IIMAGE, IVOLUME) Procedural wrappers odcm = obj_new('idlffdicom')
The 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
The IDLffDICOM Class: Example 2 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 idlffdicom_ex2.pro The attribute referenced by (0008,0060) lists the modality of the device used to create the image.
The IDLffDICOM Class: Example 3 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.
The IDLffDICOM Class: Example 4 tv, *image, /order tvscl, *image, /order iimage, *image, /order ivolume, image_stack, /order idlffdicom_ex4a.pro idlffdicom_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 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: Problems 1. 1.What is the patient's name in CT_2.dcm in the course files? What is the referring physician's name? 2. 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. 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 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 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 be specified.
The IDLffDICOMex Class: Example 2 Read image pixel data and display with IIMAGE. ; Display image pixel data with IIMAGE. iimage, odcm->getpixeldata() idlffdicomex_ex2.pro Note that the GetPixelData method automatically flips the image data; setting the ORDER keyword to IIMAGE (or TV or TVSCL) is not necessary.
The IDLffDICOMex Class: Example 3 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=ts ts1 = odcm->getvalue('0002,0010') endif idlffdicomex_ex3a.pro idlffdicomex_ex3b.pro Use QueryValue to test for an attribute's existence. An error results from attempting to access an attribute that doesn't exist.
The IDLffDICOMex Class: Example 4 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=type odcm->commit idlffdicomex_ex4.pro Original DICOM files (i.e., directly from a scanner) should not be modified. Instead, the file should be cloned and labeled as such.
The IDLffDICOMex Class: Example 5 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 idlffdicomex_ex5.pro
The IDLffDICOMex Class: Problems 1. 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. 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. 3.Clone CR_1.dcm and reduce the size of the file by selecting a transfer syntax that supports compression. 4. 4.Read the file ctscan.dat from the examples/data subdirectory and create a new (nonconforming) DICOM file with its image pixel data.
Further Information For more information on IDL's DICOM 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
Images 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.
Outline 1. 1.IDL arrays and diagnostic/statistical routines 2. 2.Pixel operations stretching/scaling, window center and width, thresholding, masking, negation, mathematical operations, equalization 3. 3.Neighborhood operations smoothing, median filtering, convolution, sharpening, edge detection 4. 4.Global transforms Fourier and Radon transforms 5. 5.Geometric operations rotating, scaling and resizing images; registration, fusion 6. 6.Feature extraction methods regions of interest (ROI), region growing, morphological operators, segmentation
IDL Arrays and Diagnostic / Statistical Routines
IDL 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 Routines A list of routines used to get information about an image. HELPSIZEMIN MAXWHEREARRAY_INDICES MOMENTMEDIANARRAY_EQUAL TOTALPRODUCTIMAGE_STATISTICS FINITEHISTOGRAMN_ELEMENTS UNIQSORT helpful_routines_ex.pro
Histogram The histogram is the graph of the frequency distribution of an image's pixel values. Integrate the histogram over pixel value to obtain the cumulative histogram. image histogram histogram_ex.pro h = histogram(image, locations=x) plot, x, h c = total(h, /cumulative) plot, x, c
Problems 1. 1.Write a function that returns the range of values in an array. 2. 2.Write a function that returns the odd values of an input array. (Hint: use WHERE)
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 / Scaling Stretching or scaling enhances the contrast in an image by broadening a selected region of the image's histogram. The IDL minimum operator < returns the smaller of its two operands, the maximum operator > returns the larger. Both are array operators. scaling_ex.pro scaled_image = (image > lo) < hi original scaled
Window Center and Width The window center and width define an intensity range into which the pixels of an image are scaled. These quantities are commonly known as the brightness and contrast of the image. lo = center - width/2 hi = center + width/2 scaled_image = (image > lo) < hi window_settings_ex.pro original scaled
Arithmetic Operations Arithmetic operators, which are array operators in IDL, can be applied directly to the pixel values of an image. In this example, the range of image pixel intensities is halved, decreasing the separation of the fine details of the image. image *= 0.5 arithmetic_ex.pro original scaled
Thresholding Thresholding is a simple form of segmentation. Pixels that satisfy a relational operation are set to 1, the rest are set to 0, giving a binary image. Thresholding is typically used to 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
Masking Like thresholding, image masks are created with rela- tional operators. When applied, a mask blocks pixels beneath it, allowing the remaining pixels to pass. This example uses the WHERE function to identify pixels associated with bone and set those pixels to a new value, identified with a yellow color. i_mask = where(image ge threshold) masked_image[i_mask] = max(image) original masked image masking_ex.pro
Histogram Equalization Histogram equalization increases the contrast in an image by remapping its pixel values, spreading the image's histogram over the full range of intensity values. eq_image = hist_equal(image) equalization_ex.pro original imagehistogramequalized histogramequalized image
Problems 1. 1.Make an IDL function to set the window center and width for an image. 2. 2.Construct a mask to remove a square region of 100x100 pixels from the center of the image kidney20.dcm. 3. 3.Create a new image from kidney20.dcm where the black pixels around the edge are transparent.
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
Smoothing Smoothing removes high-frequency information from an image. Smoothing is typically used to attenuate noise or soften an image. The SMOOTH function is used to apply a rectangular running-mean (or tophat) filter to an image. smoothing_ex.pro smoothed_image = smooth(image, 5) original filtered image smoothing kernel
Median Filtering Median filtering replaces each point in an image with the median intensity value of a two- dimensional neighborhood of a given width. Median filtering removes outlying values from an image without altering or introducing new pixels into the image, thereby preserving edges in the image. median_filtering_ex.pro smoothed_image = median(image, 5) original filtered image
Convolution 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) - 1 kernel[1,1] = 8 d_image = convol(image,kernel, 9) original differentiated image Laplacian kernel
Sharpening Sharpening enhances contrast by boosting the high-frequency components of an image. Many techniques exist. Here, a highpass filtered image is added to the original image. The Laplacian kernel from the previous slide is used. sharpening_ex.pro original sharpened image hipass = convol(image, kernel, 9) sharp = image + hipass
Edge Enhancement Edge enhancement routines use differentiating kernels to increase the brightness of edges in an image. Different kernels can be used to enhance different edges in the image. r_image = roberts(image) s_image = sobel(image) original imagewith Sobel filterwith Roberts filter with horizontal difference filter edge_enhancement_ex.pro
Problems 1. 1.Write a program to display the difference between images filtered with the SMOOTH and MEDIAN functions. 2. 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. 3.Demonstrate that prior smoothing of an image can improve the results of edge enhancement routines.
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 Transform The fast Fourier transform (FFT) is an algorithm for transforming discrete data between the physical and frequency domains. IDL's FFT can operate on arrays of up to 8 dimensions; the FFT is performed on each dimension separately the same routine is used 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 inverse
Fast 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 image shifted power spectrum - log shifted power spectrum power spectrum spectrum_ex.pro
Fast Fourier Transform: Filtering Filtering is used to alter an image by attenuating, isolating or removing features of the image in the frequency domain. filters operations lowpassbandstopbandpasshighpass exponentialButterworthideal filters_and_operations_ex.pro
Fast Fourier Transform: Example Lowpass filter an image using an ideal filter with a cutoff at 1/4 the Nyquist frequency for the image. image_hat = fft(image) cutoff = (xsize < ysize) / 8 filter = dist(xsize,ysize) le cutoff image_lowpass = fft(image_hat*filter, /inverse) imagefiltered image filtered power spectrum lowpass_ex.pro
Radon Transform The Radon transform is used in reconstructing imagery from CT scanners. 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) imagecontrastbackprojectionedge-enhancedRadon transform
Problems 1. 1.Sharpen an image using Fourier filtering. Compare the results with those from SHARPENING_EX in the previous section.
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 Images IDL provides built-in array operators and array manipulation routines 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
Registration Registration is a procedure for determining the best spatial fit for two or more images that overlap a scene. Methods: cross-correlation control points registration_ex1.pro registration_ex2.pro image 2image 1 calcium map
Image Fusion Image fusion is a procedure for melding images from different modalities that overlap the same scene. IDL application: the IDLmedFusion class fuses two images, using one of 23 blending functions, producing an indexed or an RGB image as a result. idlmedfusion__define.pro fusion_image_demo.pro
Problems 1. 1.Crop the text from US_10.dcm in the MIPI image directory.
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) 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 vertices enclosing the subset. IDL has built-in tools for defining ROIs both interactively and algorithmically.
Regions of Interest (ROI) Manual or interactive ROI definition: – –XROI – –IIMAGE Automatic or algorithmic ROI definition: – –CONTOUR – –SEARCH2D / SEARCH3D – –REGION_GROW Quantification: – –IDLanROI class – –IMAGE_STATISTICS
Regions of Interest (ROI) 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 1 Interactively define an ROI with XROI, then compute statistics and define a mask with IDLanROI. xroi_ex.pro
Regions 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 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 methodmultiplier method
Morphological Operations Morphological operators emphasize shapes in binary (b) or grayscale (g) images, based on the geometry of a structuring kernel. 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 1 The morphological closing operation fills small gaps in a binary or grayscale image. morph_close_ex.pro close threshold bmoz = moz le 250B kernel = replicate(1,3,3) bmoz = morph_close(bmoz, kernel)
Morphological 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 r grad = morph_gradient(img, disc) original
Segmentation 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: 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)
Problems 1. 1.Define ROIs from the two regions created in the example program REGION_GROW_EX. 2. 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. 3.Use LABEL_REGION to count the cells in rbcells.jpg in the examples/data subdirectory.
References 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.
Contact Information RSI Global Services 4990 Pearl East Circle Boulder, CO 80301 tel +1 303 786 9900 fax +1 303 786 9909 firstname.lastname@example.org email@example.com http://www.rsinc.com