Centroid Neural Network with Spatial Constraints

A noise deduction method for image data by using centroid neural network (CNN) is proposed in this paper. The spatial constraints are applied to neural network(CNN) algorithm to reduce noise in image data. The proposed algorithm incorporates a novel approach of using the weights of attributes to evaluate the roles of the latter. Experiments and results on a set of synthetic data show that the proposed algorithm provides superior performance over other algorithms such as fuzzy C-means(FCM)and fuzzy C-means with spatial constraints(FCM-S).

Since the phenomena of intensity inhomogeneity in MR images are prominent and adversely affect quantitative image analysis .In this paper ,we propose a novel magnetic resonance (MR) image segmentation approach based on the kernel graph cuts technique .Because of automatic multiregion segmentation and global energy minimization ,the kernel graph cuts method can be applied to many kinds of images segmentation ,such as MR images and so on . To reduce or eliminate intensity inhomogeneity in MR images ,we add the intensity inhomogeneity correction step which is based on fuzzy c-means (FCM) algorithm before the segment procedure .Firstly , the real MR image data obtained after bias corrected by FCM algorithm .Secondly ,we segment the real MR image data by kernel graph cuts method .Experiments show that the kernel graph cuts method with intensity inhomogeneity correction have a better segment result in accuracy and over-segmentation .

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

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.

A Fuzzy C-Means (FCM)-Based Approach for Computerized Segmentation of Breast Lesions in Dynamic Contrast-Enhanced MR Images 1

In this paper, various medical software tools, which have been developed for the manual, semi-automatic or fully-automatic segmentation of magnetic resonance (MR) images, and some features of these software tools are presented and discussed. MR imaging is one of the medical imaging modalities which are frequently preferred by the clinicians and surgeons. Segmentation of the MR images in clinical researches and in clinical engineering-based studies is emerging as a considerable requirement for the analysis of the disease images and the diseases. Medical software tools have been developed to meet this need and to provide facilities for the clinicians, surgeons and researchers in computer-aided research projects they conduct. Currently available MR image segmentation tools have many differences when considering criteria such as design, graphical-user interface and functions they provide. In this short review study, we briefly summarized 10 different MR image segmentation software tools highlighted in clinical researches and engineering studies and these software tools have been introduced.

In this paper, a new method is proposed to decrease sensitiveness to motion noise and uncertainty in magnetic resonance imaging (MRI) segmentation especially when only one brain image is available. The method is approached with considering spatial neighborhood information by fusing the information of pixels with their neighbors with Dempster–Shafer (DS) theory. The basic probability assignment (BPA) of each single hypothesis is obtained from the membership function of applying fuzzy c-means (FCM) clustering to the gray levels of the MRI. Then multiple hypotheses are generated according to the single hypothesis. Then we update the objective pixel’s BPA by fusing the BPA of the objective pixel and those of its neighbors to get the final result. Some examples in MRI segmentation are demonstrated at the end of the paper, in which our method is compared with some previous methods. The results show that the proposed method is more effective than other methods in motion-blurred MRI segmentation.

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.

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.

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.

Automatic Segmentation of Magnetic Resonance (MR) Images plays an important role in medical image processing. Segmentation is the process of extracting the brain tissue components such as grey matter (GM), white matter (WM) and cerebrospinal fluids (CSF). The volumetric analysis of the segmented tissues helps in determining the amount of GM loss in specific disease pathology. Among the various segmentation techniques, fuzzy c means (FCM) is the most widely used one. The performance of traditional FCM is considerably reduces in noisy MR images. However, in the clinical analysis accurate segmentation of MR image is very important and crucial for the early diagnosis and prognosis. This paper put forward an Artificial Neural Network based segmentation to map the longitudinal structural changes overtime in Frontotemporal dementia (FTD) subjects that could be a better cue to impending behavioural changes. Our proposed approach has achieved an average classification accuracy of 96.7%, 96.4% and 97.96% for GM, WM and CSF respectively.

Objectives: Non-small cell lung cancer (NSCLC), the most prevalent type of lung cancer, includes subtypes such as adenocarcinoma (ADC) and squamous cell carcinoma (SCC), which require distinct management approaches. Accurately differentiating NSCLC subtypes based on diagnostic imaging remains challenging. However, the extraction of radiomic features—such as first-order statistics (FOS), second-order statistics (SOS), and fractal dimension texture analysis (FDTA) features—from magnetic resonance (MR) images supports the development of quantitative NSCLC assessments. Methods: This study aims to evaluate whether the integration of FDTA features with FOS and SOS texture features in MR image analysis improves machine learning classification of NSCLC into ADC and SCC subtypes. The study was conducted on 274 MR images, comprising ADC (n = 122) and SCC (n = 152) cases. From the segmented MR images, 93 texture features were extracted. The random forest algorithm was used to identify informative features from both FOS/SOS and combined FOS/SOS/FDTA datasets. Subsequently, the k-nearest neighbors (kNN) algorithm was applied to classify MR images as ADC or SCC. Results: The highest performance (accuracy = 0.78, precision = 0.81, AUC = 0.89) was achieved using 37 texture features selected from the combined FOS/SOS/FDTA dataset. Conclusions: Incorporating fractal descriptors into the texture-based classification of lung MR images enhances the differentiation of NSCLC subtypes.

Recent developments in magnetic resonance (MR) imaging have opened up new avenues in the investigation of cardiovascular physiology. Inherent signal intensity of any tissue on MR images depends largely on proton concentration as well as longitudinal (T1) and transverse (T2) relaxation times. Myocardial contrast can be manipulated by using specific MR pulse sequences which are selectively sensitive to differences in any one of these parameters. Paramagnetic metal complexes are used as contrast media in MR imaging to enhance the inherent contrast. Contrast media in MR imaging are not directly visible but change the magnetic properties of other nuclei in close proximity, such as those of the water hydrogen. The signal of water can be altered by the contrast medium in different ways, either by changing the relaxation times or through bulb susceptibility effects, or both. The role of MR contrast media for quantitative characterization of ischemic heart disease has advanced considerably in the past 10 years. Conventional MR imaging techniques following the administration of contrast media are useful for identifying and sizing myocardial infarctions and for distinguishing between occlusive and reperfused myocardial infarctions as well as reversible (stunned) and irreversible injuries. Recent results suggest that contrast-enhanced MR imaging can also be used to identify dead cells in reperfused ischemically injured myocardium. The recently developed fast MR imaging techniques, with the aid of MR contrast media as a perfusion indicator, may be useful in estimating regional myocardial perfusion and blood volume. The assessment of capillary circulation or myocardial perfusion may be used for evaluating the extent of hypoperfusion and treatment efficacy. Experimental and clinical perfusion studies indicate that perfusion-sensitive MR imaging detects compromised myocardium (area at risk). Combining myocardial perfusion imaging with the anatomic and functional information provided by other MR imaging sequences could make MR imaging a comprehensive noninvasive technique for the evaluation of ischemic heart disease.

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.

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.