A novel approach to correcting for intensity nonuniformity in magnetic resonance (MR) data is described that achieves high performance without requiring a model of the tissue classes present. The method has the advantage that it can be applied at an early stage in an automated data analysis, before a tissue model is available. Described as nonparametric nonuniform intensity normalization (N3), the method is independent of pulse sequence and insensitive to pathological data that might otherwise violate model assumptions. To eliminate the dependence of the field estimate on anatomy, an iterative approach is employed to estimate both the multiplicative bias field and the distribution of the true tissue intensities. The performance of this method is evaluated using both real and simulated MR data.

/pdf/a-nonparametric-method-for-automatic-correction-of-intensity-4smqm7oz20.pdf

A nonparametric method for automatic correction of intensity nonuniformity in MRI data

A review on image segmentation techniques

Brain tumor segmentation with Deep Neural Networks

▪ Abstract Image segmentation plays a crucial role in many medical-imaging applications, by automating or facilitating the delineation of anatomical structures and other regions of interest. We present a critical appraisal of the current status of semiautomated and automated methods for the segmentation of anatomical medical images. Terminology and important issues in image segmentation are first presented. Current segmentation approaches are then reviewed with an emphasis on the advantages and disadvantages of these methods for medical imaging applications. We conclude with a discussion on the future of image segmentation methods in biomedical research.

Current methods in medical image segmentation.

MUCKE aims to mine a large volume of images, to structure them conceptually and to use this conceptual structuring in order to improve large-scale image retrieval. The last decade witnessed important progress concerning low-level image representations. However, there are a number problems which need to be solved in order to unleash the full potential of image mining in applications. The central problem with low-level representations is the mismatch between them and the human interpretation of image content. This problem can be instantiated, for instance, by the incapability of existing descriptors to capture spatial relationships between the concepts represented or by their incapability to convey an explanation of why two images are similar in a content-based image retrieval framework. We start by assessing existing local descriptors for image classification and by proposing to use co-occurrence matrices to better capture spatial relationships in images. The main focus in MUCKE is on cleaning large scale Web image corpora and on proposing image representations which are closer to the human interpretation of images. Consequently, we introduce methods which tackle these two problems and compare results to state of the art methods. Note: some aspects of this deliverable are withheld at this time as they are pending review. Please contact the authors for a preview.

http://ieeexplore.ieee.org/iel2/4524/12820/00592460.pdf

Image Processing

MRI segmentation: Methods and applications

Magnetic resonance (MR) brain section images are segmented and then synthetically colored to give visual representations of the original data with three approaches: the literal and approximate fuzzy c-means unsupervised clustering algorithms, and a supervised computational neural network. Initial clinical results are presented on normal volunteers and selected patients with brain tumors surrounded by edema. Supervised and unsupervised segmentation techniques provide broadly similar results. Unsupervised fuzzy algorithms were visually observed to show better segmentation when compared with raw image data for volunteer studies. For a more complex segmentation problem with tumor/edema or cerebrospinal fluid boundary, where the tissues have similar MR relaxation behavior, inconsistency in rating among experts was observed, with fuzz-c-means approaches being slightly preferred over feedforward cascade correlation results. Various facets of both approaches, such as supervised versus unsupervised learning, time complexity, and utility for the diagnostic process, are compared. >

A comparison of neural network and fuzzy clustering techniques in segmenting magnetic resonance images of the brain

A system that automatically segments and labels glioblastoma-multiforme tumors in magnetic resonance images (MRIs) of the human brain is presented. The MRIs consist of T1-weighted, proton density, and T2-weighted feature images and are processed by a system which integrates knowledge-based (KB) techniques with multispectral analysis. Initial segmentation is performed by an unsupervised clustering algorithm. The segmented image, along with cluster centers for each class are provided to a rule-based expert system which extracts the intracranial region. Multispectral histogram analysis separates suspected tumor from the rest of the intracranial region, with region analysis used in performing the final tumor labeling. This system has been trained on three volume data sets and tested on thirteen unseen volume data sets acquired from a single MRI system. The KB tumor segmentation was compared with supervised, radiologist-labeled "ground truth" tumor volumes and supervised K-nearest neighbors tumor segmentations. The results of this system generally correspond well to ground truth, both on a per slice basis and more importantly in tracking total tumor volume during treatment over time.

/pdf/automatic-tumor-segmentation-using-knowledge-based-4616iqmtm8.pdf

Automatic tumor segmentation using knowledge-based techniques

The authors' main contribution is to build upon their earlier efforts by expanding the tissue model concept to cover a brain volume. Furthermore, processing time is reduced and accuracy is enhanced by the use of knowledge propagation, where information derived from one slice is made available to succeeding slices as additional knowledge. The system is organized as follows. Each MR slice is initially segmented by an unsupervised fuzzy c-means clustering algorithm. Next, an expert system uses model-based recognition techniques to locate a landmark, called a focus-of attention tissue. Qualitative models of slices of brain tissue are defined and matched with their instances from imaged slices. If a significant deformation is detected in a tissue, the slice is classified to be abnormal and volume processing halts. Otherwise, the expert system locates the next focus-of-attention tissue, based on a hierarchy of expected tissues. This process is repeated until either a slice is classified as abnormal or all tissues of the slice are labeled. If the slice is determined to be abnormal, the entire volume is also considered abnormal and processing halts. Otherwise, the system will proceed to the next slice and repeat the classification steps until all slices that comprise the volume are processed. A rule-based expert system tool, CLIPS, is used to organize the system. Low level modules for image processing and high level modules for image analysis, all written in the C language, are called as actions from the right hand sides of the rules. The system described here is an attempt to provide completely automatic segmentation and labeling of normal volunteer brains. The absolute accuracy of the segmentations has not yet been rigorously established. The relative accuracy appears acceptable. Efforts have been made to segment an entire volume (rather than merging a set of segmented slices) using supervised pattern recognition techniques or unsupervised fuzzy clustering. However, there is sometimes enough data nonuniformity between slices to prevent satisfactory segmentation. >

MRI segmentation using fuzzy clustering techniques

Purpose To assess the effectiveness of two automated magnetic resonance imaging (MRI) segmentation methods in determining the gross tumor volume (GTV) of brain tumors for use in radiation therapy treatment planning. Methods and materials Two automated MRI tumor segmentation methods (supervised k-nearest neighbors [kNN] and automatic knowledge-guided [KG]) were evaluated for their potential as “cyber colleagues.” This required an initial determination of the accuracy and variability of radiation oncologists engaged in the manual definition of the GTV in MRI registered with computed tomography images for 11 glioma patients. Three sets of contours were defined for each of these patients by three radiation oncologists. These outlines were compared directly to establish inter- and intraoperator variability among the radiation oncologists. A novel, probabilistic measurement of accuracy was introduced to compare the level of agreement among the automated MRI segmentations. The accuracy was determined by comparing the volumes obtained by the automated segmentation methods with the weighted average volumes prepared by the radiation oncologists. Results Intra- and inter-operator variability in outlining was found to be an average of 20% ± 15% and 28% ± 12%, respectively. Lowest intraoperator variability was found for the physician who spent the most time producing the contours. The average accuracy of the kNN segmentation method was 56% ± 6% for all 11 cases, whereas that of the KG method was 52% ± 7% for 7 of the 11 cases when compared with the physician contours. For the areas of the contours where the oncologists were in substantial agreement (i.e., the center of the tumor volume), the accuracy of kNN and KG was 75% and 72%, respectively. The automated segmentation methods were found to be least accurate in outlining at the edges of the tumor volume. Conclusions The kNN method was able to segment all cases, whereas the KG method was limited to enhancing tumors and gliomas with clear enhancing edges and no cystic formation. Both methods undersegment the tumor volume when compared with the radiation oncologists and performed within the variability of the contouring performed by experienced radiation oncologists based on the same data.

R.P. Velthuizen

Papers

MRI segmentation: Methods and applications

A comparison of neural network and fuzzy clustering techniques in segmenting magnetic resonance images of the brain

Automatic tumor segmentation using knowledge-based techniques

MRI segmentation using fuzzy clustering techniques

Brain tumor target volume determination for radiation treatment planning through automated MRI segmentation.