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For example, the following table lists the modules that constitute an MR Image IOD: The hundred-or-so modules in DICOM describe everything that could be combined to make a DICOM information object. IODs are defined in terms of smaller functional units, or modules, which correspond to specific real-world objects, such as patients, imaging equipment, etc. dicomread understands most IODs that are listed in the DICOM specification. The DICOM files in the examples above contain MR Image Information Objects. The DICOM specification contains numerous Information Object Definitions (IODs), such as MR Image, Ultrasound Multiframe Image, and Radiotherapy Plan. Together, all of these attributes comprise an Information Object.
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The reason for this is that DICOM messages encapsulate all of the information about a medical imaging procedure, including details about the patient, study, imaging modality, and image series in addition to the image frame stored in the file. MaxPixels(p) = info.LargestImagePixelValue Īs shown in the previous example, a typical DICOM file contains numerous attributes. MinPixels(p) = info.SmallestImagePixelValue % Keep track of the minimum and maximum pixel values. The linear combination to rescale the grayscale values is "y = (x - b) * m", where b is the minimum x value and m is a constant ratio derived from the input and output ranges. We can use this metadata along with the imlincomb function in the Image Processing Toolbox to rescale the image data to fill the entire 16-bit dynamic range. We still have to take for granted that the image contains signed data&msdash a "PixelRepresentation" value of "1" indicates that type of data-but the rest of the information that we need is easily obtained. NFrames = 20 % The number of files in the directory
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The values for "Rows," "Columns," and "BitsStored" tell us exactly what we need to know, and we can rewrite the code that reads the image stack. % Preallocate the 256-by-256-by-1-by-20 image array.Īfter running this code, the MATLAB workspace contains a 4-D array with the image data, and a plot of the MR slices appears.Īs you can see, there are many metadata values, or attributes. We can read the series with the following code: (These 20 images are stored in 20 DICOM files with names such as brain_017.dcm, which you can download from MATLAB Central if you want to run the examples.) Let's suppose we know that each image is 256-by-256 and contains signed 16-bit data. Suppose that we have a study consisting of a series of 20 transverse MRI brain images and we want to read them into MATLAB. Each series is performed on a single modality such as an MR, CT, or X-ray device and can have multiple related images.
In medical imaging, a patient is subject to an imaging study, which may contain multiple series of images.
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Software such as MATLAB that supports DICOM can share images with all of these devices, provided that each of the hardware devices has implemented the necessary DICOM services. Integration isn't limited to just hardware.
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For example, it is possible to integrate a MR scanner from GE Medical Systems with a picture archive system (PACS) from Agfa and another vendor's film printer without using translation devices. DICOM is the common format, easing integration of solutions from different vendors.
With the advent of DICOM as a formal standard in 1993, one protocol replaced many protocols and formats. A cottage industry developed to provide data translation services. This process was chaotic and fraught with difficulty. Integrating medical hardware and software from different vendors meant translating from one vendor's protocols to another's.
Before DICOM, each manufacturer used proprietary image formats and communications protocols to connect their hardware solutions with third-party products. DICOM has significantly improved communication between medical devices and lowered the cost and complexity of integrating hardware and software solutions.