Analysis of Alzheimer MR brain images using entropy based segmentation and Minkowski Functional
23 May 2014-pp 1-6
TL;DR: In this work, an attempt has been made to analyze atrophy of MR brain images using Minkowski Functionals (MFs) of the entropy based skull stripped whole brain image to help diagnose Alzheimer conditions in the brain.
Abstract: In this work, an attempt has been made to analyze atrophy of MR brain images using Minkowski Functionals (MFs) of the entropy based skull stripped whole brain image. The normal and Alzheimer images considered in this work are obtained from MIRIAD database. The proposed algorithm uses Shannon entropy and Tsallis entropy methods to calculate the global and local threshold values for the edge detection. The obtained edges map are further processed using morphological operation. The mask generated from the edge map is used to extract the brain tissues. The performance of skull stripping is validated by correlating the total brain area and ground truth. The accuracy of entropy based skull stripping is compared with Otsu thresholding method. The structural changes in skull stripped brain images are analysed using Minkowski functionals such as area, perimeter and Euler number. Results show that the entropy based method is able to extract the total brain. The correlation of total brain area with ground truth is high (R = 0.93). It is also observed that the Minkowski functional, Euler number gives significant discrimination (p<;0.001) of normal and Alzheimer subjects. Hence, the entropy based method along with Minkowski functionals could be used for diagnosis of Alzheimer conditions in the brain.
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01 Feb 2021-Microsystem Technologies-micro-and Nanosystems-information Storage and Processing Systems
TL;DR: In the proposed method thresholds for segmenting the MR image are computed by maximizing the mutual information for the two features, compactness and homogeneity by maximizingThe proposed algorithm is tested against the real T1 MR image to asses the accuracy.
Abstract: Magnetic resonance (MR) brain image segmentation is an important task for the early detection of any deformation followed by the quantitative analysis for the prediction and stage defection of brain diseases. But segmentation of the MR brain image suffers from limited accuracy as captured images have non-uniform homogeneity over an organ, presence of noise, uneven and broken boundary etc. Due to the complex structure of the brain and varieties of the captured MR images, only a single feature based MR image segmentation cannot give sufficient accurate result. In the proposed method thresholds for segmenting the MR image are computed by maximizing the mutual information for the two features, compactness and homogeneity. The proposed algorithm is tested against the real T1 MR image to asses the accuracy. Further the output is validated and compared with the ground truth and other recently reported works.
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37,017 citations
"Analysis of Alzheimer MR brain imag..." refers background or methods in this paper
...A comprehensive survey of a variety of thresholding techniques has been carried out and it has been shown that Otsu’s method gives better threshold selection[10]....
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...The selection of an adequate threshold of gray-level for extracting objects from their background is important [10]....
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TL;DR: A sequence of low-level operations to isolate and classify brain tissue within T1-weighted magnetic resonance images (MRI) using a combination of anisotropic diffusion filtering, edge detection, and mathematical morphology is described.
Abstract: We describe a sequence of low-level operations to isolate and classify brain tissue within T1-weighted magnetic resonance images (MRI). Our method first removes nonbrain tissue using a combination of anisotropic diffusion filtering, edge detection, and mathematical morphology. We compensate for image nonuniformities due to magnetic field inhomogeneities by fitting a tricubic B-spline gain field to local estimates of the image nonuniformity spaced throughout the MRI volume. The local estimates are computed by fitting a partial volume tissue measurement model to histograms of neighborhoods about each estimate point. The measurement model uses mean tissue intensity and noise variance values computed from the global image and a multiplicative bias parameter that is estimated for each region during the histogram fit. Voxels in the intensity-normalized image are then classified into six tissue types using a maximum a posteriori classifier. This classifier combines the partial volume tissue measurement model with a Gibbs prior that models the spatial properties of the brain. We validate each stage of our algorithm on real and phantom data. Using data from the 20 normal MRI brain data sets of the Internet Brain Segmentation Repository, our method achieved average kappa indices of kappa = 0.746 +/- 0.114 for gray matter (GM) and kappa = 0.798 +/- 0.089 for white matter (WM) compared to expert labeled data. Our method achieved average kappa indices kappa = 0.893 +/- 0.041 for GM and kappa = 0.928 +/- 0.039 for WM compared to the ground truth labeling on 12 volumes from the Montreal Neurological Institute's BrainWeb phantom.
978 citations
"Analysis of Alzheimer MR brain imag..." refers methods in this paper
...(2001) have presented a modified Marr- Hildreth edge detection technique for skull stripping [9]....
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TL;DR: Tsallis entropy is applied as a general entropy formalism for information theory and for the first time image thresholding by nonextensive entropy is proposed regarding the presence of nonadditive information content in some image classes.
Abstract: Image analysis usually refers to processing of images with the goal of finding objects presented in the image. Image segmentation is one of the most critical tasks in automatic image analysis. The nonextensive entropy is a recent development in statistical mechanics and it is a new formalism in which a real quantity q was introduced as parameter for physical systems that present long range interactions, long time memories and fractal-type structures. In image processing, one of the most efficient techniques for image segmentation is entropy-based thresholding. This approach uses the Shannon entropy originated from the information theory considering the gray level image histogram as a probability distribution. In this paper, Tsallis entropy is applied as a general entropy formalism for information theory. For the first time image thresholding by nonextensive entropy is proposed regarding the presence of nonadditive information content in some image classes. Some typical results are presented to illustrate the influence of the parameter q in the thresholding.
490 citations
"Analysis of Alzheimer MR brain imag..." refers methods in this paper
...Thresholding by nonextensive Tsallis entropy has been used to separate object and the background using the luminance value [11]....
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...level thresholding [11] is described as ( ) arg max[ ( ) ( ) (1 ) ( )....
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TL;DR: The 3-D extension of the Marr-Hildreth operator is described, and it is shown that its zero crossings are related to anatomical surfaces.
Abstract: Algorithms for 3-D segmentation and reconstruction of anatomical surfaces from magnetic resonance imaging (MRI) data are presented. The 3-D extension of the Marr-Hildreth operator is described, and it is shown that its zero crossings are related to anatomical surfaces. For an improved surface definition, morphological filters-dilation and erosion-are applied. From these contours, 3-D reconstructions of skin, bone, brain, and the ventricular system can be generated. Results obtained with different segmentation parameters and surface rendering methods are presented. The fidelity of the generated images comes close to anatomical reality. It is noted that both the convolution and the morphological filtering are computationally expensive, and thus take a long time on a general-purpose computer. Another problem is assigning labels to the constituents of the head; in the current implementation, this is done interactively. >
329 citations
"Analysis of Alzheimer MR brain imag..." refers methods in this paper
...Semi-automated techniques which use edge detection followed by morphological operation have been attempted [7]....
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TL;DR: The approach the authors take is to model a prelabeled brain atlas as a physical object and give it elastic properties, allowing it to warp itself onto regions in a preprocessed image.
Abstract: The authors describe a computerized method to automatically find and label the cortical surface in three-dimensional (3-D) magnetic resonance (MR) brain images. The approach the authors take is to model a prelabeled brain atlas as a physical object and give it elastic properties, allowing it to warp itself onto regions in a preprocessed image. Preprocessing consists of boundary-finding and a morphological procedure which automatically extracts the brain and sulci from an MR image and provides a smoothed representation of the brain surface to which the deformable model can rapidly converge. The authors' deformable models are energy-minimizing elastic surfaces that can accurately locate image features. The models are parameterized with 3-D bicubic B-spline surfaces. The authors design the energy function such that cortical fissure (sulci) points on the model are attracted to fissure points on the image and the remaining model points are attracted to the brain surface. A conjugate gradient method minimizes the energy function, allowing the model to automatically converge to the smoothed brain surface. Finally, labels are propagated from the deformed atlas onto the high-resolution brain surface.
318 citations
"Analysis of Alzheimer MR brain imag..." refers methods in this paper
...Minkowski Functionals are computed by binarizing the segmented brain by applying several threshold levels between its minimum and maximum intensity limits [8]....
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...Sandor and Leahy (1997) developed an automated edge-detection technique using Marr-Hildreth edge detection and morphological operation steps to extract the whole brain [8]....
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