Rationale and Objectives
This article provides a survey of segmentation methods for medical images. Usually, classification of segmentation methods is done based on the approaches adopted and the domain of application.
Materials and Methods
This survey is conducted on the recent segmentation methods used in biomedical image processing and explores the methods useful for better segmentation. A critical appraisal of the current status of semiautomated and automated methods is made for the segmentation of anatomical medical images emphasizing the advantages and disadvantages. Computer-aided diagnosis (CAD) used by radiologists as a second opinion has become one of the major research areas in medical imaging and diagnostic radiology. A picture archiving communication system (PACS) is an integrated workflow system for managing images and related data that is designed to streamline operations throughout the whole patient care delivery process.
Results
By using PACS, the medical image interpretation may be changed from conventional hard-copy images to soft-copy studies viewed on the systems workstations.
Conclusion
The automatic segmentations assist the doctors in making quick diagnosis. The CAD need not be comparable to that of physicians, but is surely complementary.
Tumor segmentation from magnetic resonance imaging (MRI) data is an important but time-consuming manual task performed by medical experts. Automating this process is a challenging task because of the high diversity in the appearance of tumor tissues among different patients and in many cases similarity with the normal tissues. MRI is an advanced medical imaging technique providing rich information about the human soft-tissue anatomy. It has several advantages over other imaging techniques, enabling it to provide three-dimensional data with high contrast between soft tissues. However, the amount of data is far too much for manual analysis, which has been one of the biggest obstacles in the effective use of MRI. For this reason, automatic or semiautomatic techniques of computer-aided image analysis are necessary. Segmentation of MRIs into different tissue classes, especially gray matter, white matter, and cerebrospinal fluid, is an important task. Brain MRIs have a number of features: first, they are statistically simple and are theoretically piecewise constant with a small number of classes. Second, they have relatively high contrast between different tissues. Unlike many other medical imaging modalities, the contrast in an MRI depends strongly on the way the image is acquired. By adding radio frequency or gradient pulses and by carefully choosing relaxation timings, it is possible to highlight different components in the object being imaged and produce high-contrast images. These two features facilitate segmentation. On the other hand, ideal imaging conditions are never realized in practice. The piecewise-constant property is degraded considerably by electronic noise, the bias field, and the partial-volume effect, all of which cause classes to overlap in the image intensity histogram. A wide variety of approaches have been proposed for brain MRI segmentation. These can be roughly divided into two categories: structural and statistical. Structural methods are based on the spatial properties of the image such as edges and regions. Various edge detection algorithms have been applied to extract boundaries between different brain tissues . However, such algorithms are vulnerable to artifacts and noise. Region growing is another popular structural approach. Starting from a totally different viewpoint, statistical methods label pixels according to probability values, which are determined based on the intensity distribution of the image. In their simplest form, thresholding-based methods are always chosen for scenes containing solid objects, resting on a background with intensities well separated from the objects. However, this is generally not effective for brain MRIs. Therefore, thresholding-based methods are unlikely to produce reliable results . Most statistical approaches rely on certain assumptions or models of the probability distribution function of the image intensities and its associated class labels, which can both be considered random variables. A statistical approach can either be parametric or nonparametric. Both are widely used in segmentation of brain MRIs. In nonparametric methods, the density model relies entirely on the data itself (ie, no prior assumption is made about the functional form of the distribution, but a large number of correctly labeled training points are required in advance). One of the most widely used nonparametric methods is K-Nearest-Neighbors. Nonparametric methods are adaptive, but suffer from the difficulty of obtaining a large number of training points, which can be tedious and a heavy burden even for experienced people. Clearly, such methods are not fully automatic. Unlike nonparametric approaches, parametric approaches rely on an explicit functional form of the intensity density function. For brain MRIs, the only method developed to date is based on the finite mixture model; in particular, the finite Gaussian mixture model when the Gaussian likelihood distribution is assumed .
Computer-aided diagnosis (CAD) has become a part of the routine clinical work for the detection of breast cancer on mammograms at many screening sites and hospitals in the United States. This indicates that CAD is beginning to be applied widely in the detection and differential diagnosis of many different types of abnormalities in medical images obtained in various examinations by use of different imaging modalities. In fact, CAD has become one of the major research subjects in medical imaging and diagnostic radiology . Although early attempts at computerized analysis of medical images were made in the 1960s, serious and systematic investigation on CAD began in the 1980s with a fundamental change in the concept for utilization of the computer output from automated computer diagnosis to CAD. Here, the motivation for development of CAD schemes is presented together with the current status and future potential of CAD in the environment of picture archiving communication system (PACS) . With the increasing availability of digitized images, CAD is a hot topic of research today. The basic concept is to provide a computer output as a second opinion to assist image interpretation by radiologists toward improving the accuracy and consistency of radiological diagnosis and also by reducing the image reading time. To achieve this goal, it is necessary not only to develop suitable algorithms, but also to quantify and maximize the effect of computer output on the performance of radiologists. Research and development of CAD involve a team effort by investigators–physicists, radiologists, computer scientists, engineers, psychologists, and statisticians. Use of all imaging modalities, all body parts and all kinds of examination, will have a major impact on medical imaging and diagnostic radiology .
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Literature review and research motivation
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Table 1
Summary of Clinical Evaluation Setup and Percentage Errors for Volume Measurement
Data Galima LG Galima HG Year Volume Error (%) Computation Time (Minutes) Gibbs T1E 10 1996 5 10 Letteboer 20 2004 8 — Droske T1E — 2005 Good 3 Liu FLAIR, T1, T1E 10 2005516 Vaidyanathan T1, PD, T2 4 199512 — Fletcher-Heath T1, PD, T2 6 200128 — Clark T1, PD, T2 (all with Gd) 7 19985 — Kaus SPGR-Enh 14 2001175 Moonis FLAIR 19 200117 — Mazzra T1, FLAIR (CT) 3 8 20041575 Prastawa T2 1 1 20042690
Galima LG, low grade galima; Galima HG, high grade galima; T1, longitudinal relaxation time; T2, transverse relaxation time; T1E, echo delay time; SPGR-Enh, Spoiled gradient-recalled-Enhanced; PD, proton-density; FLAIR, fluid-attenuated inversion-recovery; CT, computed tomography; Gd, Godolinium.
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Classification of segmentation methods
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Thresholding
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Region Growing
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Classifiers
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Clustering
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Artificial Neural Networks
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Markov Random Field Models
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Deformable Models
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Atlas-guided Approaches
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Watershed Methods
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Level Set Methods
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The Combination of Watershed and Level Set
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Fuzzy Connectedness
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Some Current Hot Techniques
The core neurochemical and imaging biomarkers of Alzheimer’s disease
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MRI-based volumetry
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Multiparametric tissue characterization of brain neoplasms using MRI
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Computer-assisted segmentation of white matter lesions in 3D MRI
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Segmentation of lung nodules in CT image
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Lung motion and volume measurements by dynamic 3D MRI
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Molecular positron emission tomography (PET)/CT imaging guided radiation therapy treatment planning
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Deformation registration of endorectal prostate MRI
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Detection of asthma using of multidetector CT and hyperpolarized He-3 MRI
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Automatic identification of infarct slices and hemisphere in diffusion-weighted imaging (DWI) scans
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Automated 11 C-PiB standardized uptake value ratio
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The current diagnosis packages
The Status of CAD
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Table 2
Number of Computed-assisted Diagnosis Papers Presented at the Radiological Society of North America 2000–2005
Years 2000 2001 2002 2003 2004 2005 Chest 22 37 53 94 70 48 Breast 23 28 32 37 48 49 Colon 4 10 21 17 15 30 Brain — 4 2 10 9 15 Liver 3 — 5 9 9 9 Skeletal 2 7 7 9 8 5 Vascular, etc 5 — 12 15 2 7 Total 59 86 134 191 161 163
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The Status of PACS
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Table 3
Picture Archiving Communication System Usage in Europe
Study Number Country Approximate % of Picture Archiving Communication System Usage in Europe 1 France 10 2 Poland 11 3 Lithuania 20 4 Latvia 28 5 Germany 28 6 Greece 31 7 Czech Republic 33 8 Sweden 38 9 UK 41 10 Turkey 42 11 Spain 43 12 Portugal 43 13 Italy 48 14 Netherlands 50 15 Belgium 55 16 Hungary 55 17 Norway 67 18 Finland 69.5
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The role of CAD in the PACS environment
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Conclusions
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