Derivation of SOM-Like Rules for Intensity Inhomogeneity Correction in MRI (original) (raw)
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A parametric gradient descent MRI intensity inhomogeneity correction algorithm
Pattern recognition …, 2007
Given an appropriate imaging resolution, a common Magnetic Resonance Imaging (MRI) model assumes that the object under study is composed of homogeneous tissues whose imaging intensity is constant, so that MRI produces piecewise constant images. The intensity inhomogeneity (IIH) is modeled by a multiplicative inhomogeneity field. It is due to the spatial inhomogeneity in the excitatory Radio Frequency (RF) signal and other effects. It has been acknowledged as a greater source of error for automatic segmentation algorithms than additive noise. We propose a parametric IIH correction algorithm for MRI that consists of the gradient descent of an error function related to the classification error of the IIH corrected image. The inhomogeneity field is modeled as a linear combination of 3D products of Legendre polynomials. In this letter we test both the image restoration capabilities and the classification accuracy of the algorithm. In restoration processes the adaptive algorithm is used only to estimate the inhomogeneity field. Test images to be restored are IIH corrupted versions of the BrainWeb site simulations. The algorithm image restoration is evaluated by the correlation of the restored image with the known clean image. In classification processes the algorithm is used to estimate both the inhomogeneity field and the intensity class means. The algorithm classification accuracy is tested over the images from the IBSR site. The proposed algorithm is compared with Maximum A Posteriori (MAP) and Fuzzy Clustering algorithms.
Robustness of an adaptive MRI segmentation algorithm parametric intensity inhomogeneity modeling
Neurocomputing, 2009
We propose an unsupervised segmentation algorithm for magnetic resonance images (MRI) endowed with a parametric intensity inhomogeneity (IIH) correction schema and the on-line estimation of the image model intensity class means. The paper includes an extensive experimentation that shows that the algorithm is robust in the sense that it converges to good image segmentations despite the initial estimation of the image model intensity class means. The algorithm is, therefore, highly automatic requiring no interactive tuning to obtain good image segmentations, an appealing property in clinical environments. The IIH field and intensity class means estimation consists of the gradient descent of the restoration error of the intensity corrected image. Our algorithm does not work on the logarithmic transformation of the image, thus allowing for the explicit distinction between the smooth multiplicative field and the independent and identically distributed additive noise at each image voxel.
Un-supervised MR images segmentation using SOM and anisotropic diffusion filter
2015
The primary aim in brain image segmentation is to perform partition a given brain image into different regions which are homogeneous with some criterion. Magnetic resonance image (MRI) segmentation plays crucial role in accurate representation of white matter (WM), grey matter (GM) and cerebrospinal fluid (CSF) provides a way to identify many brain disorders, such as Alzheimer’s disease, schizophrenia or dementia. In this paper presents a unsupervised method for MR image segmentation based on Self Organizing Maps (SOMs). the proposed method is consist of five stages these are image acquisition, pre-processing step contain anisotropic diffusion filter and contrast limited adaptive histogram equalization(CLAHE), feature extraction using haralick features, feature selection using principle component analysis(PCA) and tissue classification using SOM. Our proposed method is performed over real MR data provided by Internet Brain Repository (IBSR 2.0) database. Performance evaluation using...
Segmentation of MR images with intensity inhomogeneities
A statistical model to segment clinical magnetic resonance (MR) images in the presence of noise and intensity inhomogeneities is proposed. Inhomogeneities are considered to be multiplicative low-frequency variations of intensities that are due to the anomalies of the magnetic fields of the scanners. The measurements are modeled as a Gaussian mixture where inhomogeneities present a bias field in the distributions. The piecewise contiguous nature of the segmentation is modeled by a Markov random field (MRF). A greedy algorithm based on the iterative conditional modes (ICM) algorithm is used to find an optimal segmentation while estimating the model parameters. Results with simulated and hand-segmented images are presented to compare performance of the algorithm with other statistical methods. Segmentation results with MR head scans acquired from four different clinical scanners are presented. ᭧ 1998 Elsevier Science B.V.
Parametric estimate of intensity inhomogeneities applied to MRI
IEEE Transactions on Medical Imaging, 2000
This paper presents a new approach to the correction of intensity inhomogeneities in Magnetic Resonance Imaging (MRI) that significantly improves intensity-based tissue segmentation. The distortion of the image brightness values by a low-frequency bias field impedes visual inspection and segmentation. The new correction method called PABIC (PArametric BIas field Correction) is based on a simplified model of the imaging process, a parametric model of tissue class statistics, and a polynomial model of the inhomogeneity field. We assume that the image is composed of pixels assigned to a small number of categories with a priori known statistics. Further we assume that the image is corrupted by noise and a low-frequency inhomogeneity field. The estimation of the parametric bias field is formulated as a non-linear energy minimization problem using an Evolution Strategy. The resulting bias field is independent of the image region configurations and thus overcomes limitations of methods based on homomorphic filtering. Further, PABIC can correct bias distortions much larger than the image contrast. Input parameters are the intensity statistics of the classes and the degree of the polynomial function. The polynomial approach combines bias correction with histogram adjustment, making it well-suited for normalizing the intensity histogram of datasets from serial studies.
Compensation of spatial inhomogeneity in MRI based on a parametric bias estimate
1996
This paper presents a new approach to the correction of intensity inhomogeneities in Magnetic Resonance Imaging (MRI) that significantly improves intensity-based tissue segmentation. The distortion of the image brightness values by a low-frequency bias field impedes visual inspection and segmentation. The new correction method called PABIC (PArametric BIas field Correction) is based on a simplified model of the imaging process, a parametric model of tissue class statistics, and a polynomial model of the inhomogeneity field. We assume that the image is composed of pixels assigned to small number of categories with a-priori known statistics and the image is corrupted by noise and a low-frequency inhomogeneity field. The estimation of the parametric bias field is formulated as a non-linear energy minimization problem using an Evolution Strategy. The resulting bias field is independent of the image region configurations and thus overcomes limitations of methods based on homomorphic filtering. Further, PABIC can correct bias distortions much larger than the image contrast. Input parameters are the intensity statistics of the classes and the degree of the polynomial function. The polynomial approach combines bias correction with histogram adjustment, making it well suited for normalizing the intensity histogram of datasets from serial studies.
IEEE Transactions on Image Processing, 2000
Intensity inhomogeneity often occurs in real-world images, which presents a considerable challenge in image segmentation. The most widely used image segmentation algorithms are region-based and typically rely on the homogeneity of the image intensities in the regions of interest, which often fail to provide accurate segmentation results due to the intensity inhomogeneity. This paper proposes a novel region-based method for image segmentation, which is able to deal with intensity inhomogeneities in the segmentation. First, based on the model of images with intensity inhomogeneities, we derive a local intensity clustering property of the image intensities, and define a local clustering criterion function for the image intensities in a neighborhood of each point. This local clustering criterion function is then integrated with respect to the neighborhood center to give a global criterion of image segmentation. In a level set formulation, this criterion defines an energy in terms of the level set functions that represent a partition of the image domain and a bias field that accounts for the intensity inhomogeneity of the image. Therefore, by minimizing this energy, our method is able to simultaneously segment the image and estimate the bias field, and the estimated bias field can be used for intensity inhomogeneity correction (or bias correction). Our method has been validated on synthetic images and real images of various modalities, with desirable performance in the presence of intensity inhomogeneities. Experiments show that our method is more robust to initialization, faster and more accurate than the well-known piecewise smooth model. As an application, our method has been used for segmentation and bias correction of magnetic resonance (MR) images with promising results.
Improved Intensity Inhomogeneity Correction Techniques in MR Brain Image Segmentation
Proceedings of the 17th IFAC World Congress, 2008, 2008
Intensity inhomogeneity or intensity non-uniformity (INU) is an undesired phenomenon that represents the main obstacle for MR image segmentation and registration methods. Various techniques have been proposed to eliminate or compensate the INU, most of which are embedded into clustering algorithms. This paper proposes a pre-filtering technique for Gaussian and impulse noise elimination, and a smoothening filter that assists the fuzzy c-means (FCM) algorithm at the estimation of inhomogeneity as a slowly varying additive or multiplicative noise. The segmentation is produced by FCM algorithm together with the INU estimation. The slowly varying behaviour of the bias or gain field is assured by a smoothening filter that performs a context dependent averaging, based on a morphological criterion. The experiments using 2-D synthetic phantoms and real MR images show, that the proposed method provides accurate segmentation. The produced segmentation and fuzzy membership values can serve as excellent support for 3-D registration and segmentation techniques.
Brain MR Image Segmentation Using Self Organizing Map
In this paper a novel brain MR image segmentation method is presented based on self organizing map (SOM) neural network. An accurate segmentation of brain tissues provides a way to identify many brain disorders. This paper presents unsupervised approaches for brain image segmentation. The proposed method consists of four stages. Initially an anisotropic diffusion filtering is used as a pre-processing step to eliminate bias field and random noise. Then Stationary wavelet transform (SWT) is applied to the images to obtain multi-resolution information for distinguishing different tissues. Statistical information of the different tissues is extracted by applying spatial filtering to the coefficients of SWT. These features are combined together with the raw wavelet transform coefficients to obtain a feature vector. This feature vector is applied to the SOM network. SOM is used to segment images in a competitive unsupervised training methodology. The output images show that the proposed m...
Adaptive segmentation of MRI data
IEEE Transactions on Medical Imaging, 1996
Intensity-based classi cation of MR images has proven problematic, even when advanced techniques are used. Intra-scan and inter-scan intensity inhomogeneities are a common source of di culty. While reported methods have had some success in correcting intra-scan inhomogeneities, such methods require supervision for the individual scan. This paper describes a new method called adaptive segmentation that uses knowledge of tissue intensity properties and intensity inhomogeneities to correct and segment MR images. Use of the EM algorithm leads to a method that allows for more accurate segmentation of tissue types as well as better visualization of MRI data, that has proven to be e ective in a study that includes more than 1000 brain scans. Implementation and results are described for segmenting the brain in the following types of images: axial (dual-echo spin-echo), coronal (3DFT gradient-echo T1-weighted) all using a conventional head coil; and a sagittal section acquired using a surface coil.