25,390 research outputs found
Automated Detection of Regions of Interest for Brain Perfusion MR Images
Images with abnormal brain anatomy produce problems for automatic
segmentation techniques, and as a result poor ROI detection affects both
quantitative measurements and visual assessment of perfusion data. This paper
presents a new approach for fully automated and relatively accurate ROI
detection from dynamic susceptibility contrast perfusion magnetic resonance and
can therefore be applied excellently in the perfusion analysis. In the proposed
approach the segmentation output is a binary mask of perfusion ROI that has
zero values for air pixels, pixels that represent non-brain tissues, and
cerebrospinal fluid pixels. The process of binary mask producing starts with
extracting low intensity pixels by thresholding. Optimal low-threshold value is
solved by obtaining intensity pixels information from the approximate
anatomical brain location. Holes filling algorithm and binary region growing
algorithm are used to remove falsely detected regions and produce region of
only brain tissues. Further, CSF pixels extraction is provided by thresholding
of high intensity pixels from region of only brain tissues. Each time-point
image of the perfusion sequence is used for adjustment of CSF pixels location.
The segmentation results were compared with the manual segmentation performed
by experienced radiologists, considered as the reference standard for
evaluation of proposed approach. On average of 120 images the segmentation
results have a good agreement with the reference standard. All detected
perfusion ROIs were deemed by two experienced radiologists as satisfactory
enough for clinical use. The results show that proposed approach is suitable to
be used for perfusion ROI detection from DSC head scans. Segmentation tool
based on the proposed approach can be implemented as a part of any automatic
brain image processing system for clinical use
Automatic segmentation of magnetic resonance images of the brain
Magnetic resonance imaging (MRI) is a technique used primarily in medical settings to produce high quality images of the human body’s internal anatomy. Each image is of a thin slice through the body, with the typical distance between slices being a few millimeters. Brain segmentation is the delineation of one or more anatomical structures within images of the brain. It promotes greater understanding of spatial relationships to aid in such tasks as surgical planning and clinical diagnoses, particularly when the segmented outlines from each image slice are displayed together as a surface in three-dimensions. A review of the literature indicates that current brain segmentation methods require a trained human expert to inspect the images and decide appropriate parameters, thresholds, or regions of interest to achieve the proper segmentation. This is a tedious time-consuming task because of the large number of images involved. A truly automatic method is needed to transform brain segmentation into a practical clinical tool. This dissertation describes a novel pattern classification approach to the problem of automatically segmenting magnetic resonance images of the brain. Based on this approach, algorithms were designed and implemented to automatically segment a number of anatomical structures. These algorithms were applied to several standard image data sets of human subjects obtained from the Internet Brain Segmentation Repository (IBSR). The resulting segmentations of the lateral ventricles and the caudate nuclei were compared to reference manual segmentations done by expert radiologists. The Tanimoto similarity coefficient was very good for the lateral ventricles (0.81) and good for the caudate nuclei (0.67)
Multispectral segmentation of whole-brain MRI
Magnetic Resonance Imaging (MRI) is a widely used medical technology for diagnosis and detection of various tissue abnormalities, tumor detection, and in evaluation of either residual or recurrent tumors. This thesis work exploits MRI information acquired on brain tumor structure and physiological properties and uses a novel image segmentation technique to better delineate tissue differences.;MR image segmentation will be important in distinguishing between boundaries of different tissues in the brain. A segmentation software tool was developed that combines the different types of clinical MR images and presents them as a single colored image. This technique is based on the fuzzy c-means (FCM) clustering algorithm. The MR data sets are used to form five-dimensional feature vectors. These vectors are segmented by FCM into six tissue classes for normal brains and nine tissue classes for human brains with tumors. The segmented images are then compared with segmentation performed using Statistical Parametric Mapping (SPM2)---software that is commonly used for brain tissue segmentation. The results from segmenting the whole volume MRI using FCM show better distinction between tumor tissues than SPM2
Magnetic Resonance Image segmentation using Pulse Coupled Neural Networks
The Pulse Couple Neural Network (PCNN) was developed by Eckhorn to model the observed synchronization of neural assemblies in the visual cortex of small mammals such as a cat. In this dissertation, three novel PCNN based automatic segmentation algorithms were developed to segment Magnetic Resonance Imaging (MRI) data: (a) PCNN image \u27signature\u27 based single region cropping; (b) PCNN - Kittler Illingworth minimum error thresholding and (c) PCNN -Gaussian Mixture Model - Expectation Maximization (GMM-EM) based multiple material segmentation. Among other control tests, the proposed algorithms were tested on three T2 weighted acquisition configurations comprising a total of 42 rat brain volumes, 20 T1 weighted MR human brain volumes from Harvard\u27s Internet Brain Segmentation Repository and 5 human MR breast volumes. The results were compared against manually segmented gold standards, Brain Extraction Tool (BET) V2.1 results, published results and single threshold methods. The Jaccard similarity index was used for numerical evaluation of the proposed algorithms. Our quantitative results demonstrate conclusively that PCNN based multiple material segmentation strategies can approach a human eye\u27s intensity delineation capability in grayscale image segmentation tasks
A Fetal Brain magnetic resonance Acquisition Numerical phantom (FaBiAN)
Accurate characterization of in utero human brain maturation is critical as it involves complex and interconnected structural and functional processes that may influence health later in life. Magnetic resonance imaging is a powerful tool to investigate equivocal neurological patterns during fetal development. However, the number of acquisitions of satisfactory quality available in this cohort of sensitive subjects remains scarce, thus hindering the validation of advanced image processing techniques. Numerical phantoms can mitigate these limitations by providing a controlled environment with a known ground truth. In this work, we present FaBiAN, an open-source Fetal Brain magnetic resonance Acquisition Numerical phantom that simulates clinical T2-weighted fast spin echo sequences of the fetal brain. This unique tool is based on a general, flexible and realistic setup that includes stochastic fetal movements, thus providing images of the fetal brain throughout maturation comparable to clinical acquisitions. We demonstrate its value to evaluate the robustness and optimize the accuracy of an algorithm for super-resolution fetal brain magnetic resonance imaging from simulated motion-corrupted 2D low-resolution series compared to a synthetic high-resolution reference volume. We also show that the images generated can complement clinical datasets to support data-intensive deep learning methods for fetal brain tissue segmentation
An Inductive Transfer Learning Approach using Cycle-consistent Adversarial Domain Adaptation with Application to Brain Tumor Segmentation
With recent advances in supervised machine learning for medical image
analysis applications, the annotated medical image datasets of various domains
are being shared extensively. Given that the annotation labelling requires
medical expertise, such labels should be applied to as many learning tasks as
possible. However, the multi-modal nature of each annotated image renders it
difficult to share the annotation label among diverse tasks. In this work, we
provide an inductive transfer learning (ITL) approach to adopt the annotation
label of the source domain datasets to tasks of the target domain datasets
using Cycle-GAN based unsupervised domain adaptation (UDA). To evaluate the
applicability of the ITL approach, we adopted the brain tissue annotation label
on the source domain dataset of Magnetic Resonance Imaging (MRI) images to the
task of brain tumor segmentation on the target domain dataset of MRI. The
results confirm that the segmentation accuracy of brain tumor segmentation
improved significantly. The proposed ITL approach can make significant
contribution to the field of medical image analysis, as we develop a
fundamental tool to improve and promote various tasks using medical images
Pulse Coupled Neural Networks for the Segmentation of Magnetic Resonance Brain Images
This research develops an automated method for segmenting Magnetic Resonance (MR) brain images based on Pulse Coupled Neural Networks (PCNN). MR brain image segmentation has proven difficult, primarily due to scanning artifacts such as interscan and intrascan intensity inhomogeneities. The method developed and presented here uses a PCNN to both filter and segment MR brain images. The technique begins by preprocessing images with a PCNN filter to reduce scanning artifacts. Images are then contrast enhanced via histogram equalization. Finally, a PCNN is used to segment the images to arrive at the final result. Modifications to the original PCNN model are made that drastically improve performance while greatly reducing memory requirements. These modifications make it possible to extend the method to filter and segment three dimensionally. Volumes represented as series of images are segmented using this new method. This new three dimensional segmentation technique can be used to obtain a better segmentation of a single image or of an entire volume. Results indicate that the PCNN shows promise as an image analysis tool
Segmentation of Magnetic Resonance Brain Images Using Watershed Algorithm
An important area of current research is obtaining more information about
brain structure and function. Brain tissue is particularly complex structure and
its segmentation is an important step for studies intemporal change, detection
of morphology as well as visualization in surgical planning, volume estimation
of objects of interest, and more could benefit enormously from segmentation.
Magnetic resonance imaging (MRI) is a noninvasive method for producing
tomographic images of the human brain. Its Segmentation is problematic due to
radio frequency inhomogeneity, caused by inaccuracies in the magnetic
resonance scanner and by movement of the patient which produce intensity
variation over the image, and that makes every segmentation method fail.
The aim of this work is the development of a segmentation technique for
efficient and accurate segmentation of MR brain images. The proposed
technique based on the watershed algorithm, which is applied to the gradient
magnitude of the MRI data. The watershed segmentation algorithm is a very
powerful segmentation tool, but it also has difficulty in segmenting MR images
due to noise and shading effect present. The known drawback of the watershed
algorithm, over-segmentation, is strongly reduced by making the system
interactive (semi-automatic), by placing markers manually in the region of
interest which is the brain as well as in the background. The background
markers are needed to define the external contours of the brain. The final part
of the segmentation takes place once the gradient magnitudes of the MRI data
are calculated and markers have been obtained from each region. Catchment’s
basins originate from each of the markers, resulting in a common line of
separation between brain and surrounding.
The proposed segmentation technique is tested and evaluated on brain images
taken from brainweb. Brainweb is maintained by the Brain Imaging Center at
the Montreal Neurological Institute. The images had a combination of noise and
intensity non-uniformity (INU). By making the system semi-automatic, a good
segmentation result was obtained under all the conditions (different noise
levels and intensity non uniformity). It is also proven that the placement of
internal and external markers into regions of interest (i.e. making the system
interactive) can easily cope with the over-segmentation problem of the
watershed
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