3,495 research outputs found
Analysis of fuzzy clustering and a generic fuzzy rule-based image segmentation technique
Many fuzzy clustering based techniques when applied to image segmentation do not incorporate spatial relationships of the pixels, while fuzzy rule-based image segmentation techniques are generally application dependent. Also for most of these techniques, the structure of the membership functions is predefined and parameters have to either automatically or manually derived. This paper addresses some of these issues by introducing a new generic fuzzy rule based image segmentation (GFRIS) technique, which is both application independent and can incorporate the spatial relationships of the pixels as well. A qualitative comparison is presented between the segmentation results obtained using this method and the popular fuzzy c-means (FCM) and possibilistic c-means (PCM) algorithms using an empirical discrepancy method. The results demonstrate this approach exhibits significant improvements over these popular fuzzy clustering algorithms for a wide range of differing image types
Segmentation of articular cartilage and early osteoarthritis based on the fuzzy soft thresholding approach driven by modified evolutionary ABC optimization and local statistical aggregation
Articular cartilage assessment, with the aim of the cartilage loss identification, is a crucial task for the clinical practice of orthopedics. Conventional software (SW) instruments allow for just a visualization of the knee structure, without post processing, offering objective cartilage modeling. In this paper, we propose the multiregional segmentation method, having ambitions to bring a mathematical model reflecting the physiological cartilage morphological structure and spots, corresponding with the early cartilage loss, which is poorly recognizable by the naked eye from magnetic resonance imaging (MRI). The proposed segmentation model is composed from two pixel's classification parts. Firstly, the image histogram is decomposed by using a sequence of the triangular fuzzy membership functions, when their localization is driven by the modified artificial bee colony (ABC) optimization algorithm, utilizing a random sequence of considered solutions based on the real cartilage features. In the second part of the segmentation model, the original pixel's membership in a respective segmentation class may be modified by using the local statistical aggregation, taking into account the spatial relationships regarding adjacent pixels. By this way, the image noise and artefacts, which are commonly presented in the MR images, may be identified and eliminated. This fact makes the model robust and sensitive with regards to distorting signals. We analyzed the proposed model on the 2D spatial MR image records. We show different MR clinical cases for the articular cartilage segmentation, with identification of the cartilage loss. In the final part of the analysis, we compared our model performance against the selected conventional methods in application on the MR image records being corrupted by additive image noise.Web of Science117art. no. 86
An Interactive Medical Image Segmentation Framework Using Iterative Refinement
Image segmentation is often performed on medical images for identifying
diseases in clinical evaluation. Hence it has become one of the major research
areas. Conventional image segmentation techniques are unable to provide
satisfactory segmentation results for medical images as they contain
irregularities. They need to be pre-processed before segmentation. In order to
obtain the most suitable method for medical image segmentation, we propose a
two stage algorithm. The first stage automatically generates a binary marker
image of the region of interest using mathematical morphology. This marker
serves as the mask image for the second stage which uses GrabCut on the input
image thus resulting in an efficient segmented result. The obtained result can
be further refined by user interaction which can be done using the Graphical
User Interface (GUI). Experimental results show that the proposed method is
accurate and provides satisfactory segmentation results with minimum user
interaction on medical as well as natural images.Comment: 19 pages, 19 figures, Submitted for review in Computers in Biology
and Medicin
GPU-Based Fuzzy C-Means Clustering Algorithm for Image Segmentation
In this paper, a fast and practical GPU-based implementation of Fuzzy
C-Means(FCM) clustering algorithm for image segmentation is proposed. First, an
extensive analysis is conducted to study the dependency among the image pixels
in the algorithm for parallelization. The proposed GPU-based FCM has been
tested on digital brain simulated dataset to segment white matter(WM), gray
matter(GM) and cerebrospinal fluid (CSF) soft tissue regions. The execution
time of the sequential FCM is 519 seconds for an image dataset with the size of
1MB. While the proposed GPU-based FCM requires only 2.33 seconds for the
similar size of image dataset. An estimated 245-fold speedup is measured for
the data size of 40 KB on a CUDA device that has 448 processors
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Semi-Automatic Segmentation of Vertebral Bodies in MR Images of Human Lumbar Spines.
We propose a semi-automatic algorithm for the segmentation of vertebral bodies in magnetic resonance (MR) images of the human lumbar spine. Quantitative analysis of spine MR images often necessitate segmentation of the image into specific regions representing anatomic structures of interest. Existing algorithms for vertebral body segmentation require heavy inputs from the user, which is a disadvantage. For example, the user needs to define individual regions of interest (ROIs) for each vertebral body, and specify parameters for the segmentation algorithm. To overcome these drawbacks, we developed a semi-automatic algorithm that considerably reduces the need for user inputs. First, we simplified the ROI placement procedure by reducing the requirement to only one ROI, which includes a vertebral body; subsequently, a correlation algorithm is used to identify the remaining vertebral bodies and to automatically detect the ROIs. Second, the detected ROIs are adjusted to facilitate the subsequent segmentation process. Third, the segmentation is performed via graph-based and line-based segmentation algorithms. We tested our algorithm on sagittal MR images of the lumbar spine and achieved a 90% dice similarity coefficient, when compared with manual segmentation. Our new semi-automatic method significantly reduces the user's role while achieving good segmentation accuracy
Artificial Neural Network Fuzzy Inference System (ANFIS) For Brain Tumor Detection
Detection and segmentation of Brain tumor is very important because it
provides anatomical information of normal and abnormal tissues which helps in
treatment planning and patient follow-up. There are number of techniques for
image segmentation. Proposed research work uses ANFIS (Artificial Neural
Network Fuzzy Inference System) for image classification and then compares the
results with FCM (Fuzzy C means) and K-NN (K-nearest neighbor). ANFIS includes
benefits of both ANN and the fuzzy logic systems. A comprehensive feature set
and fuzzy rules are selected to classify an abnormal image to the corresponding
tumor type. Experimental results illustrate promising results in terms of
classification accuracy. A comparative analysis is performed with the FCM and
K-NN to show the superior nature of ANFIS systems.Comment: 5 page
A Novel Method for Automatic Segmentation of Brain Tumors in MRI Images
The brain tumor segmentation on MRI images is a very difficult and important
task which is used in surgical and medical planning and assessments. If experts
do the segmentation manually with their own medical knowledge, it will be
time-consuming. Therefore, researchers propose methods and systems which can do
the segmentation automatically and without any interference. In this article,
an unsupervised automatic method for brain tumor segmentation on MRI images is
presented. In this method, at first in the pre-processing level, the extra
parts which are outside the skull and don't have any helpful information are
removed and then anisotropic diffusion filter with 8-connected neighborhood is
applied to the MRI images to remove noise. By applying the fast bounding
box(FBB) algorithm, the tumor area is displayed on the MRI image with a
bounding box and the central part is selected as sample points for training of
a One Class SVM classifier. A database is also provided by the Zanjan MRI
Center. The MRI images are related to 10 patients who have brain tumor. 100
T2-weighted MRI images are used in this study. Experimental results show the
high precision and dependability of the proposed algorithm. The results are
also highly helpful for specialists and radiologists to easily estimate the
size and position of a tumor
A Simple Method to improve Initialization Robustness for Active Contours driven by Local Region Fitting Energy
Active contour models based on local region fitting energy can segment images
with intensity inhomogeneity effectively, but their segmentation results are
easy to error if the initial contour is inappropriate. In this paper, we
present a simple and universal method of improving the robustness of initial
contour for these local fitting-based models. The core idea of proposed method
is exchanging the fitting values on the two sides of contour, so that the
fitting values inside the contour are always larger (or smaller) than the
values outside the contour in the process of curve evolution. In this way, the
whole curve will evolve along the inner (or outer) boundaries of object, and
less likely to be stuck in the object or background. Experimental results have
proved that using the proposed method can enhance the robustness of initial
contour and meanwhile keep the original advantages in the local fitting-based
models
Image segmentation using fuzzy LVQ clustering networks
In this note we formulate image segmentation as a clustering problem. Feature vectors extracted from a raw image are clustered into subregions, thereby segmenting the image. A fuzzy generalization of a Kohonen learning vector quantization (LVQ) which integrates the Fuzzy c-Means (FCM) model with the learning rate and updating strategies of the LVQ is used for this task. This network, which segments images in an unsupervised manner, is thus related to the FCM optimization problem. Numerical examples on photographic and magnetic resonance images are given to illustrate this approach to image segmentation
Automated detection of brain abnormalities in neonatal hypoxia ischemic injury from MR images.
We compared the efficacy of three automated brain injury detection methods, namely symmetry-integrated region growing (SIRG), hierarchical region splitting (HRS) and modified watershed segmentation (MWS) in human and animal magnetic resonance imaging (MRI) datasets for the detection of hypoxic ischemic injuries (HIIs). Diffusion weighted imaging (DWI, 1.5T) data from neonatal arterial ischemic stroke (AIS) patients, as well as T2-weighted imaging (T2WI, 11.7T, 4.7T) at seven different time-points (1, 4, 7, 10, 17, 24 and 31 days post HII) in rat-pup model of hypoxic ischemic injury were used to assess the temporal efficacy of our computational approaches. Sensitivity, specificity, and similarity were used as performance metrics based on manual ('gold standard') injury detection to quantify comparisons. When compared to the manual gold standard, automated injury location results from SIRG performed the best in 62% of the data, while 29% for HRS and 9% for MWS. Injury severity detection revealed that SIRG performed the best in 67% cases while 33% for HRS. Prior information is required by HRS and MWS, but not by SIRG. However, SIRG is sensitive to parameter-tuning, while HRS and MWS are not. Among these methods, SIRG performs the best in detecting lesion volumes; HRS is the most robust, while MWS lags behind in both respects
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