An improved fuzzy c-means-raindrop optimizer for brain magnetic resonance image segmentation

Fuzzy c-means (FCM) is a popular clustering method for image segmentation. However, FCM has difficulties in handling artifacts in brain magnetic resonance imaging (MRI), especially when it comes to bias field and noise. We propose a novel multiple-surface-approximation-based FCM with interval membership method for simultaneous bias correction and segmentation of Brain MRI. First, multiple surface representation of bias field is embedded into FCM to estimate and correct bias field. Then memberships of the improved FCM are extended to intervals. After the extension, clustering centers of different MR brain tissues could be solved more properly by the proposed method. Moreover, the proposed method is less sensitive to noise by introducing effects of neighboring pixels. Experiments conducted on artificial images and synthetic and real clinical Brain MRI show that the proposed method is effective and obtains better results of both bias field correction and segmentation than comparing methods.

The segmentation of magnetic resonance (MR) brain images is an important problem in medical imaging. Accurate segmentation of MR brain images allows a detail study of 3D brain tissue anatomy. It is also of great interest in the study of many brain disorders, where accurate volumetric measurement of the disorders is often required. In view of the importance of the task, much effort has been spent on finding accurate and efficient algorithms for the MRI segmentation problem. This chapter attempts to give the readers an overview of the MR brain segmentation problem, the various image artifacts that are often encountered, and describe some of the current approaches in this area, as well as our own work. To facilitate discussion, we broadly divide current MR brain image segmentation algorithms into three categories: classification-based, region-based, and contour-based methods, and discuss the merits and limitations of these approaches. Following a review of existing methods, we describe our approach for MR brain image segmentation in detail. Our approach is based on a clustering-for-classification framework, using a novel variant of the fuzzy c-means algorithm. We show that by incorporating two key ideas into the clustering algorithm, we are able to take into account the local spatial context, to compensate for the intensity nonuniformity artifact and the partial volume averaging artifact, and to reduce the influence of image noise, during the segmentation process. Extensive experiment results on both simulated and real MR brain images are given to illustrate the effectiveness and robustness of our approach. We conclude this chapter by pointing to some possible future directions in this area.

Short stature is a common concern that necessitates pediatric endocrinology evaluation. Growth hormone (GH) deficiency is often included as an etiology. Brain and pituitary Magnetic Resonance Imaging (MRI) with gadolinium-based contrast agents (GBCAs) is the imaging modality of choice in assessing patients with GH deficiency. Given the significant strides made in MRI technology that allow improved spatial and contrast resolution, the necessity of using contrast material when obtaining brain and pituitary MRI in cases of short stature and isolated GH deficiency should be reassessed. We preformed a retrospective review of otherwise healthy patients with short stature and/or GH deficiency who underwent brain and pituitary MRI without and with contrast, to assess the benefit of contrast administration. Introduction: Short stature is a common concern that necessitates pediatric endocrinology evaluation. The etiologies of short stature are diverse. GH deficiency is often included as an etiology although it accounts for only 1-2% of short stature cases. The prevalence of GH deficiency is reported to be ~ 1:3500. The vast majority of GH deficiency cases are idiopathic in nature with only 20% due to organic causes. The organic causes of GH deficiency include congenital central nervous system (CNS) anomalies, tumors and other pathologic conditions that involve the pituitary-hypothalamic region. As a result, the radiological assessment of the hypothalamic-pituitary region is considered standard of care for evaluating patients with GH deficiency. Although brain and pituitary MRI is the imaging modality of choice in assessing patients with GH deficiency, its yield in cases of isolated GH deficiency is very low. In a study of 40 otherwise normal patients with isolated GH deficiency, 35 (87.5%) had normal brain MRIs. The abnormal findings of brain MRI in the minority of isolated GH deficiency cases included pituitary hypoplasia, pituitary stalk agenesis, lack of the normal T1-weighted pituitary hyperintensity in the posterior part of the sella turcica, and the presence of a high-intensity signal at the infundibular level representing ectopic neurohypophysis. Traditionally, these brain and pituitary MRI images are obtained with the use of contrast material (gadolinium). The main purpose of using contrast material is for the evaluation of pituitary microadenomas. Given the fact that significant strides made in MRI technology and pituitary microadenomas are not appeared to be associated with GH deficiency, the necessity of using contrast material when obtaining brain and pituitary MRI in cases of short stature and isolated GH deficiency should be reassessed. GBCAs have been shown to deposit in different tissues including the kidneys and the brain. The risk increases with repeated doses. The clinical significance of this deposition is unclear at this time but warrants caution especially in pediatric population who have a longer expected lifespan to manifest any delayed effects. Allergic reactions and gastrointestinal symptoms in pediatric patients can occur with GBCA administration, although the incidence is low. Using contrast material also increases the total cost of the MRI study and prolongs the time needed to complete it. Moreover, in order to use contrast material, intravenous venous (IV) access is required which causes discomfort and additional stress to children and their families. Therefore, we performed a retrospective review of otherwise healthy patients with short stature and/or growth hormone deficiency who underwent brain and pituitary MRI without and with contrast, to assess whether contrast administration led to diagnoses that would have otherwise been missed and/or impacted the patient’s clinical course.Objectives: - To compare the diagnostic yield of non-contrast MRI with pre and post-contrast MRI of the brain and pituitary in evaluation of pediatric patients with short stature and/or growth hormone deficiency.- A secondary objective is to measure the size of the pituitary gland and correlates it with peak growth hormone levels (using insulin/argenine). Methodology: We included patients who underwent brain/pituitary MRI with/without contrast performed at our institution between Jan 2013-Dec 2018 who have short stature/GH deficiency. We excluded patients with known diagnosis of other pituitary hormone deficiencies prior to obtaining MRI studies, genetic and neurological disorders, known tumors/malignancies of any type, or renal failure. Two pediatric neuroradiologists independently reviewed the brain and pituitary MRI of these patients (each read 50% of the cohort) blinded to the clinical data and diagnoses. Each radiologist initially reviewed only the non-contrast portions of the studies, and subsequently, the same radiologist reviewed the entire study, including pre- and post-contrast portions in a separate session. The two sessions were 6 weeks apart to avoid recall bias. Several imaging findings including size and morphology of pituitary gland, presence of congenital anomalies or focal lesions and any associated intracranial findings systematically recorded, and subsequently analyzed. Hypotheses: 1.The incidence of finding congenital pituitary cysts is the same when obtaining brain/pituitary MRI imaging using gadolinium contrast versus when not using contrast in patients with short stature and or isolate GH deficiency. 2.The incidence of discovering abnormal infundibulum is the same when obtaining brain/pituitary MRI imaging using gadolinium contrast versus when not using contrast in patients with short stature and or isolate GH deficiency. 3.Small pituitary size correlate with GH deficiency. Results: -We identified 327 patients with short stature/GH deficiency from Jan 2013-Dec 2018-224 (68.5%) are males and 103 (31.5%) are females. -The mean age at the time of imaging is 10 years and the median is 11 years. -161 (49.24%) have height z-score < -2.25 and 166 (50.76%) have height z-score > -2.25.-82 (25.07%) have IGF1 z-score for age < -2, 102 (31.19%) have z-score ≥-2 to ≤ -1, 141 (43.12%) have z-score > -1 and 2 (0.62%) have no level done.-63 (19.27%) have GH peak <5, 87 (26.61%) have GH peak 5-7.99, 53 (16.21%) have GH peak 8-9.99, 30 (9.17%) have GH peak > 10 and 94 (28.75%) did not undergo GH provocative testing. -The kappa coefficient for pars intermedia cyst on pre vs. post contrast imaging is 0.74 and 0.55 for the infundibulum on pre vs. post contrast imaging. -The mean pituitary height for patients with IGF z-score < -2 is 3.9 mm, 4 mm for z-score ≥- 2 to ≤ -1 and 4.3 mm for z-score > -1-The mean pituitary height for patients with peak GH < 5 is 3.8 mm, 4.2 mm for peak 5-7.99, 4.3 mm for peak 8-9.99 and 4.4 mm for peak > 10. Conclusion: This question has not been answered or even raised in the literature. Our findings suggest that the there is no added benefit to use gadolinium when obtaining brain/pituitary MRI for the evaluation of GH deficiency/short stature. Furthermore, it seems that there is an association between the pituitary height and the GH status of the cohort which is in line with previous published studies.

Improved evidential fuzzy c-means method

Medical imaging is a technique to get images of the human body for medical science or clinical purposes. Segmentation of a medical image is a challenging task to isolate the suspicious region from the complex medical images. Genetic algorithms (GAs) are an effective tool to handle the problem of medical image segmentation. In this chapter, an application of color magnetic resonance (MR) brain image segmentation is presented by the parallel optimized multilevel sigmoidal (ParaOptiMUSIG) activation function with the parallel self-organizing neural network (PSONN) architecture. Not only confined within this approach, color MR brain image is also segmented by the NSGA-II-based ParaOptiMUSIG activation function to incorporate the multiple objective function-based scenario. These methods are compared with the process of color MR brain image segmentation by the MUSIG activation function with the PSONN architecture. All the methods are applied on a real-life color MR brain image and the quality of the segmented images are accessed by four standard objective functions. The comparison shows that the ParaOptiMUSIG activation function-based method and the NSGA-II-based ParaOptiMUSIG activation function-based method perform better than the MUSIG activation function-based method to segment the color MR brain image.

Brain MRI high resolution image creation and segmentation with the new GAN method

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

In this paper, we developed a new deformable model, the charged fluid model (CFM), that uses the simulation of a charged fluid to segment anatomic structures in magnetic resonance (MR) images of the brain. Conceptually, the charged fluid behaves like a liquid such that it flows through and around different obstacles. The simulation evolves in two steps governed by Poisson's equation. The first step distributes the elements of the charged fluid within the propagating interface until an electrostatic equilibrium is achieved. The second step advances the propagating front of the charged fluid such that it deforms into a new shape in response to the image gradient. This approach required no prior knowledge of anatomic structures, required the use of only one parameter, and provided subpixel precision in the region of interest. We demonstrated the performance of this new algorithm in the segmentation of anatomic structures on simulated and real brain MR images of different subjects. The CFM was compared to the level-set-based methods [Caselles et al. (1993) and Malladi et al (1995)] in segmenting difficult objects in a variety of brain MR images. The experimental results in different types of MR images indicate that the CFM algorithm achieves good segmentation results and is of potential value in brain image processing applications.

An automated hybrid approach using clustering and nature inspired optimization technique for improved tumor and tissue segmentation in magnetic resonance brain images

Image segmentation is a very crucial step in medical image analysis which is the first and the most important task in many clinical interventions. The authors propose in this paper to apply the variable neighborhood search (VNS) metaheuristic on the problem of brain magnetic resonance images (MRI) segmentation. In fact, by reviewing the literature, they notice that when the number of classes increases the computational time of the exhaustive methods grows exponentially with the number of required classes. That's why they exploit the VNS algorithm to optimize two maximizing thresholding functions which are the between-class variance (the Otsu's function) and the entropy thresholding (the Kapur's function). Thus, two versions of the VNS metaheuristic are respectively obtained: the VNS-Otsu and the VNS-Kapur. These two novel proposed thresholding methods are tested on a set of benchmark brain MRI to show their robustness and proficiency.

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.

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

To extract region of interests (ROI) in brain magnetic resonance imaging (MRI) with more than two objects and improve the segmentation accuracy, a hybrid model of a kernel-based fuzzy c-means(KFCM) clustering algorithm and Chan-Vese (CV) model for brain MRI segmentation is proposed. The approach consists of two successive stages. Firstly, the KFCM is used to make a coarse segmentation, which achieves the automatic selection of initial contour. Then an improved CV model is utilized to subdivide the image. Fuzzy membership degree from KFCM clustering is incorporated into the fidelity term of the 2-phase piecewise constant CV model to obtain accurate multi-object segmentation. Experimental results show that the proposed model has advantages both in accuracy and in robustness to noise in comparison with fuzzy c-means (FCM) clustering, KFCM, and the hybrid model of FCM and CV on brain MRI segmentation.

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.

Segmentation of brain magnetic resonance (MR) images is a vital requisite in health care applications. Spatial Fuzzy C-means (SFCM) clustering method segments the brain MR images degraded with noise and artifacts. Due to the iterative computation of the clustering parameters, the run time is high. To address this issue, we suggest a fast spatial FCM (FSFCM) clustering technique for brain MR image segmentation. The basic idea is that, the input image is processed through morphological reconstruction and local spatial similarity factor calculation prior to the clustering process. The morphological reconstruction operation helps in eliminating the noise, while inclusion of the local spatial similarity factor preserves the structural details. The proposed technique is relatively faster than SFCM because the local spatial similarity factor is to be computed only once prior to computation of the clustering parameters. The proposed method is investigated with various volumes of synthetic and clinical brain MR images. The outcomes of the experiment demonstrate better results in comparison to the state-of-the-art methods.