Analysis of Clustering Algorithms for MR Image Segmentation using IQI

In the last few years there has been rapid progress in the application of nuclear magnetic resonance clinical imaging as a diagnostic aid (Partain et al, 1983; Steiner, 1983; Worthington, 1983). Records kept by the operators of NMR imaging equipment indicate that about 1800 individuals have now been examined without incident within the United Kingdom. In view of this progress, an advisory group (Appendix) appointed by the National Radiological Protection Board recommends some modifications to the original advice given in 1981 (NRPB, 1981). The principal modifications are that: (1) It is no longer considered necessary to exclude volunteers with a history of epilepsy or cardiac disease. (2) It is not obligatory that a registered medical practitioner trained in resuscitation techniques be present during an exposure. (3) Higher limits are recommended for rates of change of magnetic flux density when the period of change is less than 10 ms. (4) The radiofrequency limit of 15 MHz has been removed. Theoretical considerations indicate that the interaction of an applied magnetic field with the intrinsic magnetic moments of protons and electrons within a molecule could alter its rate of chemical reaction (McLauchlan, 1981). Chemical reactions involving radical pair intermediates are expected to be sensitive to magnetic fields for this reason and a number of enzyme reactions that might be expected to be affected have been identified (A. Cass, personal communication). However, there is no experimental confirmation of these theoretical possibilities at present.

Image registration has become one of the most widely used techniques in computer vision. Its applications include optical flow, notion analysis, tracking, face detection, and biomedical image registration. In the present work, three different techniques of image registration were implemented and applied to both Computed Tomography (CT) and Magnetic Resonance (MR) images. The first technique is based on Cross Correlation (CC). The second approach depends on Control Points' Selection (CPS) from both the reference and the input images. The last technique is based on Maximization of Mutual Information (MMI) between We two images. The registrability is calculated for each image to measure its ability to provide unambiguous registration, by providing clear correlation peaks when registered with other subimage. Then, the three registration techniques were evaluated and compared using both the Weighted Peak Signal to Noise Ratio (WPSNR) and the Normalized Cross Correlation Coefficient (NCCC). The application of the selected techniques to CT and MR images has shown that registration based on MMI has given the best results and can be used efficiently for alignment of CT and MR images.

Segmentation of brain magnetic resonance imaging (MRI) data plays an important role in the computer-aided diagnosis and neuroscience research. Fuzzy c-means (FCM) clustering algorithm is one of the most usually used techniques for brain MRI image segmentation because of its fuzzy nature. However, the conventional FCM method fails to carry out segmentation well enough due to intensity inhomogeneity in MRI data. To overcome this issue, we propose an improved algorithm based on FCM clustering for segmentation of brain MRI data. Specifically, we modify the conventional FCM algorithm to allow for intensity inhomogeneity by introducing the regularization of the neighborhood influence and bias field. Results show that our proposed algorithm obtains reasonable segmentation of white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) from MRI data, which is superior to the expectation-maximization (EM) and conventional FCM methods.

Residual dense network for medical magnetic resonance images super-resolution

HomeRadiologyVol. 231, No. 3 PreviousNext EditorialsMR Imaging: Its Development and the Recent Nobel PrizeStephen J. RiedererStephen J. RiedererAuthor Affiliations1From the MR Laboratory, Mayo Clinic, 200 First Street SW, Rochester, MN 55905. Received January 19, 2004; accepted January 21. Address correspondence to the author (e-mail: [email protected]).Stephen J. RiedererPublished Online:Jun 1 2004https://doi.org/10.1148/radiol.2313040114MoreSectionsFull textPDF ToolsImage ViewerAdd to favoritesCiteTrack CitationsPermissionsReprints ShareShare onFacebookTwitterLinked In References 1 Lauterbur PC. Image formation by induced local interactions: examples employing nuclear magnetic resonance. Nature 1973; 242:190-191. Crossref, Google Scholar2 USA MRI market Mountain View, Calif: Frost and Sullivan Market Research, 2002. Google Scholar3 Mansfield P, Grannell PK. NMR “diffraction” in solids? J Phys C Solid State Phys 1973; 6:L422. Crossref, Google Scholar4 Mansfield P. 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Computerized fluoroscopy in real time for noninvasive visualization of the cardiovascular system: preliminary studies. Radiology 1979; 130:49-57. Link, Google ScholarArticle HistoryPublished in print: June 2004 FiguresReferencesRelatedDetailsCited ByMedicine, Vol. 93, No. 28Skeletal Radiology, Vol. 40, No. 9Medicine - Programa de Formaci?n M?dica Continuada Acreditado, Vol. 9, No. 92Magnetic Resonance Imaging, Vol. 24, No. 4Applied Magnetic Resonance, Vol. 29, No. 1Pacing and Clinical Electrophysiology, Vol. 28, No. 10Magnetic Resonance Imaging, Vol. 22, No. 10Recommended Articles RSNA Education Exhibits RSNA Case Collection Vol. 231, No. 3 Metrics Altmetric Score PDF download

Surgical robots are gaining increasing popularity because of their capability to improve the precision of pedicle screw placement. However, current surgical robots rely on unimodal computed tomography (CT) images as baseline images, limiting their visualization to vertebral bone structures and excluding soft tissue structures such as intervertebral discs and nerves. This inherent limitation significantly restricts the applicability of surgical robots. To address this issue and further enhance the safety and accuracy of robot-assisted pedicle screw placement, this study will develop a software system for surgical robots based on multimodal image fusion. Such a system can extend the application range of surgical robots, such as surgical channel establishment, nerve decompression, and other related operations. Initially, imaging data of the patients included in the study are collected. Professional workstations are employed to establish, train, validate, and optimize algorithms for vertebral bone segmentation in CT and magnetic resonance (MR) images, intervertebral disc segmentation in MR images, nerve segmentation in MR images, and registration fusion of CT and MR images. Subsequently, a spine application model containing independent modules for vertebrae, intervertebral discs, and nerves is constructed, and a software system for surgical robots based on multimodal image fusion is designed. Finally, the software system is clinically validated. We will develop a software system based on multimodal image fusion for surgical robots, which can be applied to surgical access establishment, nerve decompression, and other operations not only for robot-assisted nail placement. The development of this software system is important. First, it can improve the accuracy of pedicle screw placement, percutaneous vertebroplasty, percutaneous kyphoplasty, and other surgeries. Second, it can reduce the number of fluoroscopies, shorten the operation time, and reduce surgical complications. In addition, it would be helpful to expand the application range of surgical robots by providing key imaging data for surgical robots to realize surgical channel establishment, nerve decompression, and other operations.

Recent developments in magnetic resonance (MR) imaging have opened up new avenues in the investigation of cardiovascular physiology. Inherent signal intensity of any tissue on MR images depends largely on proton concentration as well as longitudinal (T1) and transverse (T2) relaxation times. Myocardial contrast can be manipulated by using specific MR pulse sequences which are selectively sensitive to differences in any one of these parameters. Paramagnetic metal complexes are used as contrast media in MR imaging to enhance the inherent contrast. Contrast media in MR imaging are not directly visible but change the magnetic properties of other nuclei in close proximity, such as those of the water hydrogen. The signal of water can be altered by the contrast medium in different ways, either by changing the relaxation times or through bulb susceptibility effects, or both. The role of MR contrast media for quantitative characterization of ischemic heart disease has advanced considerably in the past 10 years. Conventional MR imaging techniques following the administration of contrast media are useful for identifying and sizing myocardial infarctions and for distinguishing between occlusive and reperfused myocardial infarctions as well as reversible (stunned) and irreversible injuries. Recent results suggest that contrast-enhanced MR imaging can also be used to identify dead cells in reperfused ischemically injured myocardium. The recently developed fast MR imaging techniques, with the aid of MR contrast media as a perfusion indicator, may be useful in estimating regional myocardial perfusion and blood volume. The assessment of capillary circulation or myocardial perfusion may be used for evaluating the extent of hypoperfusion and treatment efficacy. Experimental and clinical perfusion studies indicate that perfusion-sensitive MR imaging detects compromised myocardium (area at risk). Combining myocardial perfusion imaging with the anatomic and functional information provided by other MR imaging sequences could make MR imaging a comprehensive noninvasive technique for the evaluation of ischemic heart disease.

To prospectively evaluate magnetic resonance (MR) imaging and MR spectroscopy for depiction of local prostate cancer recurrence after external-beam radiation therapy, with step-section pathologic findings as the standard of reference. Study received institutional approval, and written informed consent was obtained. Study was compliant with Health Insurance Portability and Accountability Act. Sextant biopsy, digital rectal examination, MR imaging, MR spectroscopy, and salvage radical prostatectomy with step-section pathologic examination were performed in nine patients with increasing prostate-specific antigen levels after external-beam radiation therapy. MR imaging criterion for tumor was a focal nodular region of reduced signal intensity at T2-weighted imaging. MR spectroscopic criteria for tumor were voxels with choline (Cho) plus creatine (Cr) to citrate (Cit) ratio ([Cho + Cr]/Cit) of at least 0.5 or voxels with detectable Cho and no Cit in the peripheral zone. Sensitivity and specificity of sextant biopsy, digital rectal examination, MR imaging, and MR spectroscopy were determined by using a prostate sextant as the unit of analysis. For feature analysis, MR imaging and MR spectroscopic findings were correlated with step-section pathologic findings. MR imaging and MR spectroscopy showed estimated sensitivities of 68% and 77%, respectively, while sensitivities of biopsy and digital rectal examination were 48% and 16%, respectively. MR spectroscopy appears to be less specific (78%) than the other three tests, each of which had a specificity higher than 90%. MR spectroscopic feature analysis showed that a metabolically altered benign gland could be falsely identified as tumor by using MR spectroscopic criteria; further analysis of MR spectroscopic features did not lead to improved MR spectroscopic criteria for recurrent tumor. In summary, MR imaging and MR spectroscopy may be more sensitive than sextant biopsy and digital rectal examination for sextant localization of cancer recurrence after external-beam radiation therapy.

Computer vision has been applied to many medical imaging problems. Of the different medical imaging modalities, magnetic resonance (MR) imaging is a powerful and widely used technique for medical diagnosis and is attracting much medical image processing research effort. One use of MR imaging is the detection of benign tumours, called acoustic neuromas, which occur in the auditory canal. At present, these tumours are identified manually from MR images (slices) of the head, a task which is both costly and tedious. Here, a method for automating the detection and localisation of acoustic neuromas from head MR images, using computer vision, is described which comprises of three phases: a) a data driven initial segmentation; b) classification at the pixel level, using a neural network, to identify pixels from acoustic neuromas; c) fusion of the segmentation and the pixel level classification to identify segmented regions likely to belong to an acoustic neuroma. These three phases are discussed and the results of each phase presented. Current and future work is then outlined. The MR images of the head used are 256 /spl times/ 256 pixels in size, grey scale (8 bits per pixel) images.

This thesis focuses on finding ways to differentiate the gray matter (GM) and white matter (WM) in magnetic resonance (MR) images of the human spinal cord (SC). The aim of this project is to quantify tissue loss in these compartments to study their implications on the progression of multiple sclerosis (MS). To this end, we propose segmentation algorithms that we evaluated on MR images of healthy volunteers. Segmentation of GM and WM in MR images can be done manually by human experts, but manual segmentation is tedious and prone to intra- and inter-rater variability. Therefore, a deterministic automation of this task is necessary. On axial 2D images acquired with a recently proposed MR sequence, called AMIRA, we experiment with various automatic segmentation algorithms. We first use variational model-based segmentation approaches combined with appearance models and later directly apply supervised deep learning to train segmentation networks. Evaluation of the proposed methods shows accurate and precise results, which are on par with manual segmentations. We test the developed deep learning approach on images of conventional MR sequences in the context of a GM segmentation challenge, resulting in superior performance compared to the other competing methods. To further assess the quality of the AMIRA sequence, we apply an already published GM segmentation algorithm to our data, yielding higher accuracy than the same algorithm achieves on images of conventional MR sequences. On a different topic, but related to segmentation, we develop a high-order slice interpolation method to address the large slice distances of images acquired with the AMIRA protocol at different vertebral levels, enabling us to resample our data to intermediate slice positions. From the methodical point of view, this work provides an introduction to computer vision, a mathematically focused perspective on variational segmentation approaches and supervised deep learning, as well as a brief overview of the underlying project's anatomical and medical background.

Automatic segmentation of Magnetic Resonance Images (MRI) for tissue classification becomes more challenging when the image is corrupted with noise and intensity non-uniformity (INU). Several fuzzy clustering based statistical retrospective methods exist for simultaneous correction and segmentation of images but most of them fail to be robust in presence of noise, outliers and INU artifacts. In this paper, a hybridization of rough c-means and spatial fuzzy c-means clustering is presented whose objective function has been modified to accommodate INU field as well. While the membership function of fuzzy sets enables efficient handling of overlapping partitions, the concept of lower and upper approximations of rough sets deals with uncertainty, vagueness, and incompleteness in class definition. The experiments conducted on brain MR images show promising results in terms of segmentation accuracy and class separability. The usefulness of proposed algorithm is also investigated on high field MR images. The proposed algorithm Rough-Theoretic Bias-Corrected Fuzzy C-Means Algorithm (R-BCFCM) has significant performance improvement over other similar methods from rough-fuzzy family and can be employed for MRI corrupted with high intensity non-uniformity and noise.

Parameter optimization of improved fuzzy c-means clustering algorithm for brain MR image segmentation

Comparison of Gadoxetic Acid–enhanced MR Imaging versus Four-phase Multi–detector Row Computed Tomography in Assessing Tumor Regression after Radiofrequency Ablation in Subjects with Hepatocellular Carcinomas

Kidney Detection and Segmentation in MR images allows extracting meaningful information for nephrologists, also for practical use in clinical routine, thus we should apply an fast, automatic and robust algorithm. We demonstrate the possibility of construct an algorithm that achieve these requirements. Therefore, a novel kidney segmentation algorithm was created depending on multiple stages. The Region of Interest (ROI) is extracted after we convert the input image to binary one via specific thresholding level yields from K Mean Clustering algorithm. The resulted binary image contain both of kidneys as the biggest regions, so we can isolate them after we calculate the objects areas in labeled image. Finally we can use some morphological operation to remove small objects surrounding the kidney region. The effectiveness of this method is demonstrated through experimental results on complex MR slices. Kidneys were accurately detected and segmented in a few seconds.

Recently, both Magnetic Resonance (MR) Imaging (MRI) and Spectroscopy (MRS) have emerged as promising tools for detection of prostate cancer (CaP). However, due to the inherent dimensionality differences in MR imaging and spectral information, quantitative integration of T(2) weighted MRI (T(2)w MRI) and MRS for improved CaP detection has been a major challenge. In this paper, we present a novel computerized decision support system called multimodal wavelet embedding representation for data combination (MaWERiC) that employs, (i) wavelet theory to extract 171 Haar wavelet features from MRS and 54 Gabor features from T(2)w MRI, (ii) dimensionality reduction to individually project wavelet features from MRS and T(2)w MRI into a common reduced Eigen vector space, and (iii), a random forest classifier for automated prostate cancer detection on a per voxel basis from combined 1.5 T in vivo MRI and MRS. A total of 36 1.5 T endorectal in vivo T(2)w MRI and MRS patient studies were evaluated per voxel by MaWERiC using a three-fold cross validation approach over 25 iterations. Ground truth for evaluation of results was obtained by an expert radiologist annotations of prostate cancer on a per voxel basis who compared each MRI section with corresponding ex vivo wholemount histology sections with the disease extent mapped out on histology. Results suggest that MaWERiC based MRS T(2)w meta-classifier (mean AUC, μ = 0.89 ± 0.02) significantly outperformed (i) a T(2)w MRI (using wavelet texture features) classifier (μ = 0.55 ± 0.02), (ii) a MRS (using metabolite ratios) classifier (μ = 0.77 ± 0.03), (iii) a decision fusion classifier obtained by combining individual T(2)w MRI and MRS classifier outputs (μ = 0.85 ± 0.03), and (iv) a data combination method involving a combination of metabolic MRS and MR signal intensity features (μ = 0.66 ± 0.02).