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Highlights • A bit-plane-dependence probability model is developed. • Statistical analysis of the bit-plane-dependence probability model is performed. • A fuzzy bit-plane-dependence segmentation algorithm is proposed. Abstract This paper presents a novel fuzzy bit-plane-dependence image segmentation methodology. We propose a probability model for characterizing the distributions of image variations based on bit-plane probabilities and dependencies between bit-planes. Compared with the current state-of-the-art image variation models which assume the distributions have specific structures (e.g., symmetry, monotone and periodicity), the proposed model provides a universal parametric representation that can be used to model random distributions without enforcing any specific restrictions on the distributions. In addition, we show that the maximum likelihood estimators of model parameters are joint sufficient statistics, which, in turn, justify the theoretical basis for their use. To effectively segment images with various textures, we propose a fuzzy bit-plane-dependence image segmentation algorithm. The proposed algorithm integrates the bit-plane-dependence probability model into the agglomerative fuzzy algorithm, and incorporates neighboring information and boundary correction for image segmentation applications. Experiments demonstrate the superior performance of the proposed method. [ABSTRACT FROM AUTHOR]

Multilevel thresholding techniques based on gray histogram are usually computationally expensive for the image segmentation. In this paper, we propose a novel thresholding extraction method based on variational mode decomposition (VMD). The improved VMD is used to decompose the histogram non-recursively into several sub-modes for minimizing Otsu's objective function. Then, we can extract the thresholds easily by the minimum point search (MPS) method or the cross point search (CPS) method. The experimental results demonstrate that the proposed MPS scheme exhibits more excellent capability than CPS. Further, compared with other approaches namely particle swarm optimization algorithm (PSO), fuzzy c-means (FCM) algorithm and bacterial foraging (BF) algorithm, the proposed algorithm can get similar performance, but its computing speed is faster than others. Therefore, it could have some advantages in image preprocessing, such as fast target recognition and classification. [ABSTRACT FROM AUTHOR]

The centroidal Voronoi tessellation (CVT) is a special Voronoi tessellation whose generators are also the centers of mass of the corresponding Voronoi regions. The edge-weighted centroidal Voronoi tessellation (EWCVT) greatly improves the classic CVT model by adding an edge energy term in the energy functional, and has been proven to be very effective and efficient for image segmentation. In this paper, we propose a fuzzy edge-weighted centroidal Voronoi tessellation (FEWCVT) model which generalizes the EWCVT clustering with fuzzy membership information. The FEWCVT model novelly introduces an edge energy based on fuzzy clustering and naturally combines it into the EWCVT model, and thus appropriately combines the image intensity information with the length of cluster boundaries in a fuzzy form. In its simplest form, FEWCVT model reduces to the classic EWCVT model. An iterative algorithm is proposed for the FEWCVT model based on energy minimization. In the experiments, we apply the FEWCVT method to segment various types of images and also compare it with several existing fuzzy clustering methods to demonstrate its performance. [ABSTRACT FROM AUTHOR]

Purpose: Fuzzy connectedness method (FC) is an effective method for extracting fuzzy objects from medical images. However, when FC is applied to large medical image datasets, its running time will be greatly expensive. Therefore, a parallel CUDA version of FC (CUDA-kFOE) was proposed by Ying et al. to accelerate the original FC. Unfortunately, CUDA-kFOE does not consider the edges between GPU blocks, which causes miscalculation of edge points. In this paper, an improved algorithm is proposed by adding a correction step on the edge points. The improved algorithm can greatly enhance the calculation accuracy. Methods: In the improved method, an iterative manner is applied. In the first iteration, the affinity computation strategy is changed and a look up table is employed for memory reduction. In the second iteration, the error voxels because of asynchronism are updated again. Results: Three different CT sequences of hepatic vascular with different sizes were used in the experiments with three different seeds. NVIDIA Tesla C2075 is used to evaluate our improved method over these three data sets. Experimental results show that the improved algorithm can achieve a faster segmentation compared to the CPU version and higher accuracy than CUDA-kFOE. Conclusions: The calculation results were consistent with the CPU version, which demonstrates that it corrects the edge point calculation error of the original CUDA-kFOE. The proposed method has a comparable time cost and has less errors compared to the original CUDA-kFOE as demonstrated in the experimental results. In the future, we will focus on automatic acquisition method and automatic processing. [ABSTRACT FROM AUTHOR]

Medical ultrasound (US) image segmentation and quantification can be challenging due to signal dropouts, missing boundaries, and presence of speckle, which gives images of similar objects quite different appearance. Typically, purely intensity-based methods do not lead to a good segmentation of the structures of interest. Prior work has shown that local phase and feature asymmetry, derived from the monogenic signal, extract structural information from US images. This paper proposes a new US segmentation approach based on the fuzzy connectedness framework. The approach uses local phase and feature asymmetry to define a novel affinity function, which drives the segmentation algorithm, incorporates a shape-based object completion step, and regularises the result by mean curvature flow. To appreciate the accuracy and robustness of the methodology across clinical data of varying appearance and quality, a novel entropy-based quantitative image quality assessment of the different regions of interest is introduced. The new method is applied to 81 US images of the fetal arm acquired at multiple gestational ages, as a means to define a new automated image-based biomarker of fetal nutrition. Quantitative and qualitative evaluation shows that the segmentation method is comparable to manual delineations and robust across image qualities that are typical of clinical practice. [ABSTRACT FROM AUTHOR]

Accurate segmentation of human brain image is an essential step for clinical study of magnetic resonance imaging (MRI) images. However, vagueness and other ambiguity present between the brain tissues boundaries can lead to improper segmentation. Possibilistic fuzzy c-means (PFCM) algorithm is the hybridization of fuzzy c-means (FCM) and possibilistic c-means (PCM) algorithms which overcomes the problem of noise in the FCM algorithm and coincident clusters problem in the PCM algorithm. A major challenge posed in the PFCM algorithm for segmentation of ill-defined MRI image with noise is to take into account the ambiguity in the final localization of the feature vectors due to lack of qualitative information. This may lead to improper assignment of membership (typicality) value to their desired cluster. In this paper, we have proposed the possibilistic intuitionistic fuzzy c-means (PIFCM) algorithm for Atanassov's intuitionistic fuzzy sets (A-IFS) which includes the advantages of the PCM, FCM algorithms and A-IFS. Real and simulated MRI brain images are segmented to show the superiority of the proposed PIFCM algorithm. The experimental results demonstrate that the proposed algorithm yields better result. [ABSTRACT FROM AUTHOR]

We present a self-directed method for image segmentation using a modified top-down region dividing (TDRD) approach. The TDRD-based image segmentation method solves some of the issues with histogram and region growing-based segmentation techniques. The process is efficient and achieves proper results without over segmentation or spatial-structure destruction. In this paper, we examine seven user-defined parameters of the method. These parameters are converted from human inputs to values derived from in-class information created by the algorithm allowing for autonomous image segmentation, without the need of human input or feedback. Our new autonomous implementation also reduces the computational complexity of the algorithm. This reduction will produce significant savings for the total number of computations the algorithm needs to perform image segmentation. Experimental results show that the images using these new derived values yield superior results as compared to other methods, including the original TDRD method. We compare our results visually and numerically based on the within-class standard deviation (WCSD) and the number of connected components (NCC). [ABSTRACT FROM AUTHOR]