Fuzzy Level Set Research Articles

Improved fuzzy clustering and Legendre level sets for segmentation of multiple tumours in low contrast liver CTA images

Improved fuzzy clustering and Legendre level sets for segmentation of multiple tumours in low contrast liver CTA images

Improved fuzzy clustering and Legendre level sets for segmentation of multiple tumours in low contrast liver CTA images

Accurate and fast image segmentation algorithms are of great importance in medical image processing. In this paper, a new, rapid and efficient region-based segmentation method for Liver tumour segmentation initialised using spatial FCM clustering technique is proposed. In Legendre level sets, the area of interest illumination is represented in lower dimensional subspace. A set of predefined basis functions such as Legendre basis function is used to represent the lower dimensional subspace. This kind of representation enables the robust segmentation of heterogeneous objects even in the presence of noise. The proposed algorithm has been compared with other existing algorithms and its performance evaluation is carried out in CTA abdomen images of various patients. The obtained results prove its effectiveness in low contrast inhomogeneous tumour segmentation.

A Combined Spatial Fuzzy C-Means and Level Set Approach for Endocardium Segmentation in MRI Image Series

A Combined Spatial Fuzzy C-Means and Level Set Approach for Endocardium Segmentation in MRI Image Series

Automated volumetric segmentation of retinal fluid on optical coherence tomography.

We propose a novel automated volumetric segmentation method to detect and quantify retinal fluid on optical coherence tomography (OCT). The fuzzy level set method was introduced for identifying the boundaries of fluid filled regions on B-scans (x and y-axes) and C-scans (z-axis). The boundaries identified from three types of scans were combined to generate a comprehensive volumetric segmentation of retinal fluid. Then, artefactual fluid regions were removed using morphological characteristics and by identifying vascular shadowing with OCT angiography obtained from the same scan. The accuracy of retinal fluid detection and quantification was evaluated on 10 eyes with diabetic macular edema. Automated segmentation had good agreement with manual segmentation qualitatively and quantitatively. The fluid map can be integrated with OCT angiogram for intuitive clinical evaluation.

A Novel Fuzzy Level Set Approach for Image Contour Detection

The level set methods have provided powerful frameworks for image segmentation. However, to obtain accurate boundaries of the objects, especially when they have weak edges or inhomogeneous intensities, is still a very challenging task. Actually, we have studied the popular existing level set approaches and discovered that they failed to segment the images with weak edges or inhomogeneous intensities in many cases. The weak/blurry edges and inhomogeneous intensities cause uncertainty and fuzziness for segmentation. In this paper, a novel fuzzy level set approach is proposed. At first,S-function based on the maximum fuzzy entropy principle (MEP) is used to map the image from space domain to fuzzy domain. Then, an energy function is formulated according to the differences between the actual and estimated probability densities of the intensities in different regions. A partial differential equation is derived for finding the minimum of the energy function. The proposed approach has been tested on both synthetic images and real images and evaluated by several popular metrics. The experimental results demonstrate that the proposed approach can locate the true object boundaries, even for objects with blurry boundaries, low contrast, and inhomogeneous intensities.

A New fuzzy level set method for lesion border detection in dermoscopy images

Accurate segmentation plays a vital role in automated analysis of dermoscopy images. A new method based on fuzzy level set method is introduced here for lesion detection in dermoscopy images. In this method, a hypothesis testing framework is defined first to separate lesions from skin and artifacts. Then the boundary of lesion is estimated by a decision function based on the level set energy minimization method which is driven by fuzzy connectedness concept. Performance is evaluated on real dermoscopy images which are captured from various lesions with different sizes and colors. Parameters for performance evaluation are presented and obtained results are compared with some other state-of-the-art lesion detection methods. Increased true positive rate in parallel with simultaneous decrease in false positive rate, Hammoude distance and Hausdorff distance confirm the effectiveness of proposed method for skin cancer detection.

Comparison of Two Segmentation Methods for Mammographic Image

mammography is the method of choice for early detection of breast cancer. The image segmentation aims to separate the structure of interest objects from background and other objects. Detection of breast cancer is a very crucial step in mammograms and therefore needs an accurate and standard technique for breast tumor segmentation. In the last few years, a number of algorithms have been published in the literature. Each one has their own merits and de-merits. Fuzzy-level set and wavelet with level set is proposed to facilitate mammogram image segmentation. The existing active contour models can be classified as edge-based models and region- based model. In fuzzy level set, edge based active contour model is used while, in wavelet with level set, a region-based image segmentation technique using active contours with signed pressure force function is used. Furthermore, after evaluating various parameters wavelet with level set is considered to be better than fuzzy level set, as segmentation of mass area is more effective having less error value and it shows higher PSNR as compared to other method.

Multibiometric system using fuzzy level set, and genetic and evolutionary feature extraction

This study presents a multimodal system that optimises and integrates the iris and face features based on fusion at the score level. The proposed multibiometric system has two novelties as compared with the previous work. First, the authors deploy a fuzzy C-means clustering with level set (FCMLS) method in an effort to localise the non-ideal iris images accurately. The FCMLS method incorporates the spatial information into the level set (LS)-based curve evolution approach and regularises the LS propagation locally. The proposed iris localisation scheme based on FCMLS avoids over-segmentation and performs well against blurred iris/sclera boundary. Second, genetic and evolutionary feature extraction (GEFE) is applied towards multimodal biometric recognition. GEFE uses genetic and evolutionary computation to evolve local binary pattern feature extractors to elicit distinctive features from the iris and facial images. Different weights for each modality are investigated to determine the significance of each modality. By using the FCMLS method to segment an iris image accurately, as well as using GEFE on a multibiometric dataset, the authors note improved performance of identification and verification accuracies over subjects on a unimodal dataset. More specifically, on the multimodal dataset of face and iris images, GEFE had an identification accuracy of 100%.

Mass segmentation in mammograms using a kernel-based fuzzy level set method

In this paper, a Kernel-Based Fuzzy Level Set (KFLS) method is proposed to facilitate the automatic segmentation of lesions in mammogram image. Initially, the mammogram image is pre-processed to remove the noises and to extract the breast profile. Kernel-based Fuzzy C-Means (KFCM) segmentation is applied to segment the pre-processed image into a number of clusters based on the breast structure. The cluster with Region of Interest (ROI) is automatically identified to remove background and normal tissues. Finally, the mass region extracted from the KFCM is used as an initial contour for the level set segmentation to refine the mass boundary. The result of KFCM is also used to automatically initialise the control parameters of level set segmentation algorithm. This methodology is validated on 300 mammogram images of DDSM database with diagnosed mass. The experimental results demonstrate that the proposed method achieves a sensitivity of 93.32% and accuracy of 94.31%.

Some new sets of sequences of fuzzy numbers with respect to the partial metric.

In this paper, we essentially deal with Köthe-Toeplitz duals of fuzzy level sets defined using a partial metric. Since the utilization of Zadeh's extension principle is quite difficult in practice, we prefer the idea of level sets in order to construct some classical notions. In this paper, we present the sets of bounded, convergent, and null series and the set of sequences of bounded variation of fuzzy level sets, based on the partial metric. We examine the relationships between these sets and their classical forms and give some properties including definitions, propositions, and various kinds of partial metric spaces of fuzzy level sets. Furthermore, we study some of their properties like completeness and duality. Finally, we obtain the Köthe-Toeplitz duals of fuzzy level sets with respect to the partial metric based on a partial ordering.

Iris Recognition Using Fuzzy Level Set and GEFE

Iris Recognition Using Fuzzy Level Set and GEFE

A Class of Linear Multi-Objective Decision Making Model Based on Level-2 TFNwTFC Coefficients and Its Application to Supplier Selection

In this paper, we concentrate on a class of linear multi-objective decision making models with level-2 TFNwTFC coefficients. Firstly, we define a level-2 triangular fuzzy number with a triangular fuzzy centre(TFNwTFC) based on level-2 fuzzy set theory, and propose a theorem on how to transform it into an (α, β)-level trapezoidal fuzzy number(TrFN). Secondly, in order to deal with the linear multi-objective decision making model with TrFN coefficients, we present an expected multi-objective model with chance constraints, and discuss its equivalent model. Then the ε-constraint method is employed to determine a solution to the crisp equivalent model. Finally, a supplier selection problem is used to illustrate the proposed models and approaches.

A new approach for the sequence spaces of fuzzy level sets with the partial metric

In this paper, we investigate the classical sets of sequences of fuzzy numbers by using partial metric which is based on a partial ordering. Some elementary notions and concepts for partial metric and fuzzy level sets are given. In addition, several necessary and sufficient conditions for partial completeness are established by means of fuzzy level sets. Finally, we give some illustrative examples and present some results between fuzzy and partial metric spaces.

A new kernel-based fuzzy level set method for automated segmentation of medical images in the presence of intensity inhomogeneity.

Researchers recently apply an integrative approach to automate medical image segmentation for benefiting available methods and eliminating their disadvantages. Intensity inhomogeneity is a challenging and open problem in this area, which has received less attention by this approach. It has considerable effects on segmentation accuracy. This paper proposes a new kernel-based fuzzy level set algorithm by an integrative approach to deal with this problem. It can directly evolve from the initial level set obtained by Gaussian Kernel-Based Fuzzy C-Means (GKFCM). The controlling parameters of level set evolution are also estimated from the results of GKFCM. Moreover the proposed algorithm is enhanced with locally regularized evolution based on an image model that describes the composition of real-world images, in which intensity inhomogeneity is assumed as a component of an image. Such improvements make level set manipulation easier and lead to more robust segmentation in intensity inhomogeneity. The proposed algorithm has valuable benefits including automation, invariant of intensity inhomogeneity, and high accuracy. Performance evaluation of the proposed algorithm was carried on medical images from different modalities. The results confirm its effectiveness for medical image segmentation.

A Medical Image Segmentation Based on Improved Fuzzy Clustering and Level Set

A Medical Image Segmentation Based on Improved Fuzzy Clustering and Level Set

Improved fuzzy level set method for automatic MR image segmentation based on edge competition

Improved fuzzy level set method for automatic MR image segmentation based on edge competition

Liver Segmentation from CT Image Using Fuzzy Clustering and Level Set

This paper presents a fully automatic segmentation method of liver CT scans using fuzzy c-mean clustering and level set. First, the contrast of original image is enhanced to make boundaries clearer; second, a spatial fuzzy c-mean clustering combining with anatomical prior knowledge is employed to extract liver region automatically; thirdly, a distance regularized level set is used for refinement; finally, morphological operations are used as post-processing. The experiment result shows that the method can achieve high accuracy (0.9986) and specificity (0.9989). Comparing with standard level set method, our method is more effective in dealing with over-segmentation problem.

Some Remarks on FCMLS with its Application to Foreground Extraction

It is well known that fuzzy clustering and level set are two important tools for image segmentation. The former focuses on analyzing the statistical characteristics of image features, while the latter aims to acquire the good geometrical continuity of segmentation boundaries. Obviously, two kinds of methods may be complement to each other. Inspired by this idea, a new level set model integrated with fuzzy c-means (FCM) clustering FCMLS has been presented in our previous studies. Compared with FCM and original level set methods, some remarkable characteristics and better performance have been demonstrated. In this paper, we mainly concern with the foreground extraction problem, wherein some extended works are also reported including detailed analysis on the convergence of FCMLS, multiregional FCMLS. Our research results indicate that FCMLS has good performance and is very worthful for foreground image extraction.

A Hybrid Approach for DICOM Image Segmentation Using Fuzzy Techniques

This paper provides an hybrid method for segmentation usingFCM thresholding with fuzzy levelset. FCM thresholding gives fine segmented results when compared to edge detection method. The optimization property of FCM is improved when it is combined with local thresholding. The application of Fuzzy levelset gives enhanced segmentation results.The experimental result proves that the hybrid method is efficient in statistical metrices that enhances the segmented results with fine regions .

A multi-objective decision making model for the vendor selection problem in a bifuzzy environment

In this paper, we focus on the vendor selection problem (VSP) for material supply in large-scale water conservancy and hydropower construction projects, and establish a corresponding model to solve this problem. After stating the problem, we introduce some basic knowledge of level-2 fuzzy sets and the way to transform them to normal fuzzy sets. Then we use the CCP model to handle the bifuzzy objective functions and constraints, based on a series of preparations, we transform the multi-objective integer model under bifuzzy environment into a deterministic model, then use particle swarm optimization (PSO) to solve this problem. The model is illustrated with an actual application to construction material supply.