Local Intensity Clustering Research Articles

A nonlocal energy minimization approach to brain image segmentation with simultaneous bias field estimation and denoising

Image segmentation plays an important role in medical image analysis. The most widely used image segmentation algorithms, region-based methods that typically rely on the homogeneity of image intensities in the regions of interest, often fail to provide accurate segmentation results due to the existence of bias field, heavy noise and rich structures. In this paper, we incorporate nonlocal regularization mechanism in the coherent local intensity clustering formulation for brain image segmentation with simultaneously estimating bias field and denoising, specially preserving good structures. We define an energy functional with a local data fitting term, two nonlocal regularization terms for both image and membership functions, and a $$L_2$$ L 2 image fidelity term. By minimizing the energy, we get good segmentation results with well preserved structures. Meanwhile, the bias estimation and noise reduction can also be achieved. Experiments performed on synthetic and clinical brain magnetic resonance imaging data and comparisons with other methods are given to demonstrate that by introducing the nonlocal regularization mechanism, we can get more regularized segmentation results.

TH-A-103-11: Breast Density Measurement with Cone-Beam CT and MRI: A Postmortem Study

Purpose: To investigate the feasibility of breast density quantification using a cone‐beam computed tomography (CBCT) and a magnetic resonance imaging (MRI) system with a postmortem study. Methods: Twenty pairs of postmortem breasts were imaged with a bench‐top CBCT system based on a CsI flat panel detector and an Aurora 1.5T dedicated Breast MRI system. The CT images were acquired at 50 kVp with a mean glandular dose (MGD) of approximately 3 mGy. A T1‐weighted gradient echo technique was used in MRI scans to maximize the contrast between the adipose and glandular tissues. Image processing procedures were implemented to segment the glandular tissue from adipose tissue. For CBCT images, standard histogram thresholdings based on visual assessment and automated segmentation based on a basic fuzzy c‐means (FCM) algorithm were used. For MRI images, FCM‐based clustering and the coherent local intensity clustering (CLIC) method, which estimated and removed the bias field, were used. Finally, the breast densities measured with the two systems were compared to the definitive tissue compositions obtained from chemical analysis. The linear correlation coefficient, Pearson r, was used to evaluate the two imaging modalities and the investigated image segmentation methods. Results: Both histogram thresholdings and FCM techniques used for CBCT images show excellent correlations with respect to the fibroglandular ratio (FGR) from chemical analysis, with r values of 0.964 and 0.980, respectively. The correlations of the MRI measurement were slightly worse than those from CBCT. The CLIC method successfully removed the intensity inhomogeneity induced by the bias field, and improved the r value from 0.864 to 0.915. Conclusion: CBCT can be used to accurately quantify the breast density with automated clustering algorithms. It is anticipated that CBCT may outperform breast MRI in breast density quantification due to high spatial resolution and the absence of the bias field.

Abnormality Segmentation of MRI Brain Images using Fuzzy Nearest Neighbour Approach

medical imaging techniques help in learning human brain. Magnetic resonance imaging, is a technique which is very efficient in differentiating between soft tissues. There are some techniques that accomplish the goal of tissues detection and extraction. The main objective is to provide a reliable tool to evaluate brain, by improving tissue contrast and visualization, thus reducing workload of specialist in the area. This allows a further systematic follow-up of the evolution of tumors or its treatment. A solution to this problem is offered by semi supervised anomaly detection after spatial normalization. It makes use of normal data modeling and then a distance measure and thresholding to determine abnormality in MRI. The estimation of the probability density function is usually used to treat every image as a network of locally coherent image partitions (overlapping blocks). So a strictly concave likelihood function for estimating abnormality onto each partition have been formulated and the local estimates are fused into a globally optimal estimate that satisfies the consistency constraints. Fuzzy based approach can be defined to enhance the performance of the system by providing active learning based approach. Fuzzy clustering algorithms proposed an energy-minimization approach to the coherent local intensity clustering (CLIC), with the aim of achieving tissue clustering of abnormalities properly. This approach provides efficient system for the easy detection of abnormalities.

Adaptive segmentation of magnetic resonance images with intensity inhomogeneity using level set method

It is a big challenge to segment magnetic resonance (MR) images with intensity inhomogeneity. The widely used segmentation algorithms are region based, which mostly rely on the intensity homogeneity, and could bring inaccurate results. In this paper, we propose a novel region-based active contour model in a variational level set formulation. Based on the fact that intensities in a relatively small local region are separable, a local intensity clustering criterion function is defined. Then, the local function is integrated around the neighborhood center to formulate a global intensity criterion function, which defines the energy term to drive the evolution of the active contour locally. Simultaneously, an intensity fitting term that drives the motion of the active contour globally is added to the energy. In order to segment the image fast and accurately, we utilize a coefficient to make the segmentation adaptive. Finally, the energy is incorporated into a level set formulation with a level set regularization term, and the energy minimization is conducted by a level set evolution process. Experiments on synthetic and real MR images show the effectiveness of our method.

A Level Set Method for Image Segmentation in the Presence of Intensity Inhomogeneities With Application to MRI

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.

A modified possibilistic fuzzy c-means clustering algorithm for bias field estimation and segmentation of brain MR image

A modified possibilistic fuzzy c-means clustering algorithm is presented for fuzzy segmentation of magnetic resonance (MR) images that have been corrupted by intensity inhomogeneities and noise. By introducing a novel adaptive method to compute the weights of local spatial in the objective function, the new adaptive fuzzy clustering algorithm is capable of utilizing local contextual information to impose local spatial continuity, thus allowing the suppression of noise and helping to resolve classification ambiguity. To estimate the intensity inhomogeneity, the global intensity is introduced into the coherent local intensity clustering algorithm and takes the local and global intensity information into account. The segmentation target therefore is driven by two forces to smooth the derived optimal bias field and improve the accuracy of the segmentation task. The proposed method has been successfully applied to 3 T, 7 T, synthetic and real MR images with desirable results. Comparisons with other approaches demonstrate the superior performance of the proposed algorithm. Moreover, the proposed algorithm is robust to initialization, thereby allowing fully automatic applications.

A new bias field correction method combining N3 and FCM for improved segmentation of breast density on MRI.

Quantitative breast density is known as a strong risk factor associated with the development of breast cancer. Measurement of breast density based on three-dimensional breast MRI may provide very useful information. One important step for quantitative analysis of breast density on MRI is the correction of field inhomogeneity to allow an accurate segmentation of the fibroglandular tissue (dense tissue). A new bias field correction method by combining the nonparametric nonuniformity normalization (N3) algorithm and fuzzy-C-means (FCM)-based inhomogeneity correction algorithm is developed in this work. The analysis is performed on non-fat-sat T1-weighted images acquired using a 1.5 T MRI scanner. A total of 60 breasts from 30 healthy volunteers was analyzed. N3 is known as a robust correction method, but it cannot correct a strong bias field on a large area. FCM-based algorithm can correct the bias field on a large area, but it may change the tissue contrast and affect the segmentation quality. The proposed algorithm applies N3 first, followed by FCM, and then the generated bias field is smoothed using Gaussian kernal and B-spline surface fitting to minimize the problem of mistakenly changed tissue contrast. The segmentation results based on the N3+FCM corrected images were compared to the N3 and FCM alone corrected images and another method, coherent local intensity clustering (CLIC), corrected images. The segmentation quality based on different correction methods were evaluated by a radiologist and ranked. The authors demonstrated that the iterative N3+FCM correction method brightens the signal intensity of fatty tissues and that separates the histogram peaks between the fibroglandular and fatty tissues to allow an accurate segmentation between them. In the first reading session, the radiologist found (N3+FCM > N3 > FCM) ranking in 17 breasts, (N3+FCM > N3 = FCM) ranking in 7 breasts, (N3+FCM = N3 > FCM) in 32 breasts, (N3+FCM = N3 = FCM) in 2 breasts, and (N3 > N3+FCM > FCM) in 2 breasts. The results of the second reading session were similar. The performance in each pairwise Wilcoxon signed-rank test is significant, showing N3+FCM superior to both N3 and FCM, and N3 superior to FCM. The performance of the new N3+FCM algorithm was comparable to that of CLIC, showing equivalent quality in 57/60 breasts. Choosing an appropriate bias field correction method is a very important preprocessing step to allow an accurate segmentation of fibroglandular tissues based on breast MRI for quantitative measurement of breast density. The proposed algorithm combining N3+FCM and CLIC both yield satisfactory results.