Kernel Graph Cuts Segmentation for MR Images with Intensity Inhomogeneity Correction

Clustering approach is widely used in biomedical applications particularly for brain tumor detection in abnormal magnetic resonance (MR) images. Fuzzy clustering using fuzzy C-means (FCM) algorithm proved to be superior over the other clustering approaches in terms of segmentation efficiency. But the major drawback of the FCM algorithm is the huge computational time required for convergence. The effectiveness of the FCM algorithm in terms of computational rate is improved by modifying the cluster center and membership value updation criterion. In this paper, the application of modified FCM algorithm for MR brain tumor detection is explored. Abnormal brain images from four tumor classes namely metastase, meningioma, glioma and astrocytoma are used in this work. A comprehensive feature vector space is used for the segmentation technique. Comparative analysis in terms of segmentation efficiency and convergence rate is performed between the conventional FCM and the modified FCM. Experimental results show superior results for the modified FCM algorithm in terms of the performance measures.

Segmentation of brain magnetic resonance imaging (MRI) data plays an important role in the computer-aided diagnosis and neuroscience research. Fuzzy c-means (FCM) clustering algorithm is one of the most usually used techniques for brain MRI image segmentation because of its fuzzy nature. However, the conventional FCM method fails to carry out segmentation well enough due to intensity inhomogeneity in MRI data. To overcome this issue, we propose an improved algorithm based on FCM clustering for segmentation of brain MRI data. Specifically, we modify the conventional FCM algorithm to allow for intensity inhomogeneity by introducing the regularization of the neighborhood influence and bias field. Results show that our proposed algorithm obtains reasonable segmentation of white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) from MRI data, which is superior to the expectation-maximization (EM) and conventional FCM methods.

Segmentation of white matter, grey matter and cerebrospinal fluid from brain MR images using a modified FCM based on double estimation

In the last few years there has been rapid progress in the application of nuclear magnetic resonance clinical imaging as a diagnostic aid (Partain et al, 1983; Steiner, 1983; Worthington, 1983). Records kept by the operators of NMR imaging equipment indicate that about 1800 individuals have now been examined without incident within the United Kingdom. In view of this progress, an advisory group (Appendix) appointed by the National Radiological Protection Board recommends some modifications to the original advice given in 1981 (NRPB, 1981). The principal modifications are that: (1) It is no longer considered necessary to exclude volunteers with a history of epilepsy or cardiac disease. (2) It is not obligatory that a registered medical practitioner trained in resuscitation techniques be present during an exposure. (3) Higher limits are recommended for rates of change of magnetic flux density when the period of change is less than 10 ms. (4) The radiofrequency limit of 15 MHz has been removed. Theoretical considerations indicate that the interaction of an applied magnetic field with the intrinsic magnetic moments of protons and electrons within a molecule could alter its rate of chemical reaction (McLauchlan, 1981). Chemical reactions involving radical pair intermediates are expected to be sensitive to magnetic fields for this reason and a number of enzyme reactions that might be expected to be affected have been identified (A. Cass, personal communication). However, there is no experimental confirmation of these theoretical possibilities at present.

Accurate brain tissue segmentation from magnetic resonance (MR) images is an essential step in quantitative brain image analysis. However, due to the existence of noise and intensity inhomogeneity in brain MR images, many segmentation algorithms suffer from limited accuracy. In this paper, it is assumed that the local image data within each voxel's neighborhood satisfy the Gaussian mixture model (GMM), and thus propose the fuzzy local GMM (FLGMM) algorithm for automated brain MR image segmentation. In this paper results obtained from the proposed algorithm is compared with those obtained by using Level set function in both synthetic and clinical data is analyzed. Thus concluding that the proposed algorithm can largely overcome the difficulties raised by noise, low contrast, and bias field, and improves the accuracy of brain MR image segmentation.

Medical Imaging devices facilitates enormous amount of anatomical and functional information, which aids in improved diagnosis and patient care specifically when supported by quantitative image analysis techniques. MR image pre-processing is an elementary step to ensure the favorable outcome of quantitative image analysis pipeline. The preprocessing step composes of different processes aimed to either improve the image quality or to standardize its intensity patterns. Regardless of modality specific image artefacts occurrence of intensity inhomogeneity in Magnetic Resonance Images (MRI) is prevalent which adversely affect the quantitative image analysis. In this paper a pre-processing approach for MRI sequences is introduced, where in the intensity inhomogeneity is introduced during the image acquisition process. These intensity inhomogeneities due to varying magnetic fields affect the further image analysis. Therefore to correct these discrepancies an intensity inhomogeneity correction method is applied for different MRI brain tumor scans, N4 parameters i.e non parametric, nonuniform intensity normalization technique parameters are set with B-spline grid resolution of (1, 1, 1). B-spline cube and spline distance of. 10 and number of iterations are set to 50, 40, 30 and a convergence threshold of. 1001. The proposed method is evaluated with a standard dataset of BraTs 2017 and the results satisfactory.

Image segmentation plays a preliminary and indispensable step in medical image processing. Image segmentation plays a crucial role in many medical imaging applications. In this paper, we present a novel algorithm called image Filtering spatial Kernel Fuzzy C-Means(FKFCM) for fuzzy segmentation of magnetic resonance imaging (MRI)data. The algorithm is realized by modifying the objective function in the conventional Fuzzy C-Means (FCM) algorithm using a kernel-induced distance metric, a spatial penalty on the membership functions and then using the image filtering method to correct the image. The algorithm results are compared with standard FCM, Kerneled Fuzzy C-Means (KFCM) and Spatial Fuzzy C-Means(SFCM). The performance of the proposed segmentation algorithm FKFCM provides satisfactory results compared with other algorithms.

Intensity inhomogeneity in MR images is an undesired phenomenon, which often hampers different steps of quantitative analysis such as segmentation or registration. In this paper we propose a novel fully automated method for retrospective correction of intensity inhomogeneity. The basic assumption is that inhomogeneity correction could be improved by combining the information from multiple MR channels. Intensity inhomogeneities are simultaneously removed in a four-step iterative procedure. First, the probability distribution of intensities for two channel images is calculated. In the second step, intensity correction forces, that tend to minimize image entropies, are estimated for every image voxel. Third, inhomogeneity correction fields are obtained by regularization and normalization of all voxel forces, and last, corresponding partial inhomogeneity corrections are performed separately for each channel. The method was quantitatively evaluated on simulated and real MR brain images. The results show substantial improvement in comparison with the two state-of-the-art methods.

Surgical robots are gaining increasing popularity because of their capability to improve the precision of pedicle screw placement. However, current surgical robots rely on unimodal computed tomography (CT) images as baseline images, limiting their visualization to vertebral bone structures and excluding soft tissue structures such as intervertebral discs and nerves. This inherent limitation significantly restricts the applicability of surgical robots. To address this issue and further enhance the safety and accuracy of robot-assisted pedicle screw placement, this study will develop a software system for surgical robots based on multimodal image fusion. Such a system can extend the application range of surgical robots, such as surgical channel establishment, nerve decompression, and other related operations. Initially, imaging data of the patients included in the study are collected. Professional workstations are employed to establish, train, validate, and optimize algorithms for vertebral bone segmentation in CT and magnetic resonance (MR) images, intervertebral disc segmentation in MR images, nerve segmentation in MR images, and registration fusion of CT and MR images. Subsequently, a spine application model containing independent modules for vertebrae, intervertebral discs, and nerves is constructed, and a software system for surgical robots based on multimodal image fusion is designed. Finally, the software system is clinically validated. We will develop a software system based on multimodal image fusion for surgical robots, which can be applied to surgical access establishment, nerve decompression, and other operations not only for robot-assisted nail placement. The development of this software system is important. First, it can improve the accuracy of pedicle screw placement, percutaneous vertebroplasty, percutaneous kyphoplasty, and other surgeries. Second, it can reduce the number of fluoroscopies, shorten the operation time, and reduce surgical complications. In addition, it would be helpful to expand the application range of surgical robots by providing key imaging data for surgical robots to realize surgical channel establishment, nerve decompression, and other operations.

Unsupervised Fuzzy C-Means (FCM) clustering technique has been widely used in image segmentation. However, conventional FCM algorithm, being a histogram-based method when used in classification, has an intrinsic limitation: no spatial information is taken into account. This causes the FCM algorithm to work only on well-defined images with low level of noise. In this paper, a novel improvement to fuzzy clustering is described. The prior spatial constraint, which is defined as refusable level in this paper, is introduced into FCM algorithm through Markov random field theory and its equivalent Gibbs random field theory, in which the spatial information is encoded through mutual influences of neighboring sites. The algorithm is applied to the segmentation of synthetic image and brain magnetic resonance (MR) images (simulated and real) and the classification results show the new algorithm to be insensitive to noise.

A local Zernike moment-based unbiased nonlocal means fuzzy C-Means algorithm for segmentation of brain magnetic resonance images

This work presents a detailed framework to detect the location of heart sound within the respiratory sound based on temporal fuzzy c-means (TFCM) algorithm. In the proposed method, respiratory sound is first divided into frames and for each frame, the logarithmic energy features are calculated. Then, these features are used to classify the respiratory sound as heart sound (HS containing lung sound) and non-HS (only lung sound) by the TFCM algorithm. The TFCM is the modified version fuzzy c-means (FCM) algorithm. While the FCM algorithm uses only the local information about the current frame, the TFCM algorithm uses the temporal information from both the current and the neighboring frames in decision making. To measure the detection performance of the proposed method, several experiments have been conducted on a database of 24 healthy subjects. The experimental results show that the average false-negative rate values are 0.8 ± 1.1 and 1.5 ± 1.4 %, and the normalized area under detection error curves are 0.0145 and 0.0269 for the TFCM method in the low and medium respiratory flow rates, respectively. These average values are significantly lower than those obtained by FCM algorithm and by the other compared methods in the literature, which demonstrates the efficiency of the proposed TFCM algorithm. On the other hand, the average elapsed time of the TFCM for a data with length of 0.2 ± 0.05 s is 0.2 ± 0.05 s, which is slightly higher than that of the FCM and lower than those of the other compared methods.

Image-guided regularization level set evolution for MR image segmentation and bias field correction

A Centroid Neural Network with spatial constraints (CNN-S) is proposed in this paper. The spatial constraints are applied to the Centroid Neural Network(CNN) algorithm to reduce noise in Magnetic Resonance(MR) images. MR image segmentation is performed to illustrate the application of the proposed algorithm. The proposed algorithm incorporates a novel approach of using the weights of attributes to evaluate the roles of the latter. Experiments and results on MR images from Internet Brain Segmentation Repository(IBSR) show that the proposed algorithm provides a superior performance over other algorithms such as Fuzzy C-Means(FCM)and Fuzzy C-Means with spatial constraints(FCM-S).

Intensity inhomogeneity or intensity non-uniformity (INU) is an undesired phenomenon that represents the main obstacle for magnetic resonance (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, and they generally have difficulties when INU reaches high amplitudes. This study reformulates the design of c-means clustering based INU compensation techniques by identifying and separating those globally working computationally costly operations that can be applied to gray intensity levels instead of individual pixels. The theoretical assumptions are demonstrated using the fuzzy c-means algorithm, but the proposed modification is compatible with a various range of c-means clustering based techniques. Experiments using synthetic phantoms and real MR images indicate that the proposed approach produces practically the same segmentation accuracy as the conventional formulation, but 20-30 times faster.