Image Processing and Visualization
asically all digital images are processed in one kind or another. In fact some old analog images will be processed for presentation today (Figure 15-01). Image-processing was already on its way to becoming an established field of research in its own right when clinical MR imaging equipment became available in the early 1980s.
When reading MR images, you always should be prepared for the unexpected. Digitized imaging brings more of the unexpected into your life. Still there are some easily recognizable features in most images. You recognize them immediately:
(a) When, where, and from where was this picture taken and what does it show?
(b) When and where was this picture taken?
Click to get the answers.
The digital nature of MR imaging, coupled with a wide range of applications, spurred an enormous activity in image-processing of MR imaging data in the last forty years. Computer assisted detection or diagnosis (CAD) systems were developed to find and highlight suspicious regions or structures in images of the human body by pattern recognition. They are employed in, e.g., the search for breast, lung and metastatic cancer.
In this chapter we give a brief description of image-processing techniques that have been applied to MR imaging. We also describe the somewhat related field of visualization techniques, with a special focus on 3D visualization methods. Some of the methods mentioned here relate directly to the next chapter on dynamic imaging.
Involuntarity, image processing can add to the existing delusion and bias in image reading and lead to preconceived but wrong diagnoses (Figure 15-01 and Figure 16-01). Unrecognized optical and mental illusions caused by artifacts created by image-processing algorithms may amplify such errors (Figure 15- 02). These problems are beyond the scope of this introduction to MR imaging; they are treated in detail elsewhere [⇒ Frisby].
Gray Scale and Color Images. All digital images are per se gray scale images. They can be "artifically" colored (pseudo-colors). MR images are always gray-scale images; colored MR images are only used for public relations purposes. Pictures of a number of MRI offsprings (e.g., MR angiography, dynamic contrast-enhanced MRI, functional MRI, MRI tractography, PET-MRI fusion images) often contain overlayed colored areas representing mostly the lower resolution imaging technique.
The introduction of color images is a recurrent and lasting topic in diagnostic imaging. However, the contribution of colors to imaging diagnostics, in particular high-resolution images, is much debated because colors do not add any provable diagnostic facts to an MR image. On the contrary, they might confuse, bias, and lead to a loss of information. Colors are subjective qualities. In general, their perception is not well understood. In diagnostic imaging, colors lack the dynamic range of gray scale images, image windowing, for instance, is not possible (cf. Chapter 9.).
Another problem is the human eye. In the central part of the retina, there are approximately six million cone cells which are responsible for color vision. However, 8% of the male population and 0.5% of the female population in Europe and North America suffer from a color vision deficiency. The most common one is deuteranomaly. People with this kind of relative color blindness perceive green, red and purple as a grayish shade. Radiologists or other physicians among this group reading color-coded images are unable to identify red or green colored areas of these images (a simulation is shown in Figure 15-03).
15-02 Some Fundamentals
The main objective of medical image-processing is to facilitate the gathering of or provide diagnostic information not easily seen, or not seen at all, on unprocessed images. In general, the processing of digitally acquired images is aimed at improving pictorial information for human interpretation and (or) processing data for autonomous machine perception. Both aims have been targeted in magnetic resonance imaging and in both areas successful applications have been found.
All computed image-processing requires digitized imaging. In digitized radiology, the equivalent of a regular x-ray is taken and digitized directly by a specialized x-ray system. In nuclear medicine, CT, and MR, imaging slices through the human body are acquired and subdivided into volume elements. The numerical signal from each voxel, in turn, can be translated into a distinct shade of the gray scale and be represented as a picture element in the final image (Figure 15-04). Both single images or a series of similar images can be manipulated, e.g., by noise reduction, edge or contrast enhancement [⇒ Godtliebsen].
In multichannel imaging, several channels representing n different parameters can be acquired simultaneously or by consecutive procedures, leading eventually to n images of exactly the same object (Figure 15-05).
Image-processing allows the connection of picture element data of the same location in different images with changed parameters; known connections can be computed, e.g., by using appropriate equations. Such procedures may extract additional information or allow quantification of data and thus an objective definition of structures, tissues, or metabolic processes. Several single images can, e.g., be added to a new multispectral image (synthetic image) which does not necessarily add useful information or even depict reality (Figure 15-06). Details on image processing can be found in a number of monographs, such as [⇒ Gonzalez, ⇒ Russ].
There are different ways of classifying image-processing techniques, for instance, they can be defined by what they are supposed to achieve. Types of techniques include noise reduction, image segmentation, feature extraction, and classification.
Whereas noise reduction is of vital importance for more noisy modalities like ultrasound, MR imaging has, due to a rapid development of MR hardware and software, not the same need for such techniques, perhaps with the exception of dynamic imaging (Chapter 16).
However, image segmentation and image classification have found much more widespread use in MR imaging, partly due to the possibility of acquiring multichannel data suitable for such processing.
Another important group of image-processing techniques is image registration or image alignment, which sometimes employs image-segmentation techniques to align images. Image registration is important for aligning multimodality data (for instance, nuclear medicine data and MR imaging data from the same patient) or registration of time series.
A specific type of time series (dynamic contrast-enhanced MR imaging) is described in Chapter 16. Furthermore, time series can also be used for monitoring tumor growth or growth of bones in children.
Another important type of MR imaging time series is BOLD functional MR imaging (fMRI) of the brain. Image registration is now routinely performed on fMRI data.
Improvement of diagnostic performance to reduce the ever existing level of uncertainty is one of the main propelling forces in diagnostic imaging research.
Since the human visual-perception system is unable to perform multichannel analysis in order to achieve a new dimension, image-processing developed in parallel to the introduction of MR imaging as a clinical tool. Researchers wanted to detect any message, possibly hidden, in a single MR image or a series of MR images. Given that only minimal information existed about how to approach this scientific problem, the course of research was mostly empiric.
Historically, the following main lines of approach were followed:
subtraction or overlay (superposition, data fusion) of multichannel
quantification of MR parameters, i.e., T1, T2 and proton density;
image segmentation and multispectral analysis;
Answers to the questions asked in Figure 15-01:
These questions were just asked to confuse you. You might have thought the answers were easy. Of course, they were.
The picture to the left.
Where: A bird’s-eye view of Central Park in Manhattan. Wrong: This is a vodka commercial with a vodka bottle which looks like Central Park. This picture has been image-processed.
Even if you believe that you know what you are seeing – think twice.
The picture to the right.
When: Correct – before World War II (in 1928). Where: You are wrong – not in Chicago (even if there is a commercial for the Chicago Daily News), but in Berlin (at the corner of Unter den Linden boulevard and Friedrich Strasse in the city center).
Always check the patient’s history before you read your images and make a diagnosis.
Back to Figure 15-01.