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Multilevel arthritis of the foot is a frequent problem. The arthritis does not always involve adjacent levels. A comprehensive literature search did not reveal any information about this pathology, neither about its treatment. Thi...
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Multilevel arthritis of the foot is a frequent problem. The arthritis does not always involve adjacent levels. A comprehensive literature search did not reveal any information about this pathology, neither about its treatment. This case series report presents two patients. The first patient has bilateral talonavicular and first metatarsophalangeal joint arthritis, the second has unilateral talonavicular and first tarsometatarsal joint arthritis. Conservative treatment was insufficient and operative treatment of the arthritic joints was performed using the IoFix system (Intra-Osseus Fixation Device). In all three operations arthrodesis of the talonavicular and a more distal nonadjacent joint was successfully performed. With a follow-up period up till five years postoperative, no short- nor long-term complications were observed. Multilevel arthritis with nonadjacent joints in the foot is a common pathology. Fusion of the affected joints, leaving at least one free joint in between is a surgical treatment with good results.
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In this paper, a multilevel thresholding (MT) algorithm based on the harmony search algorithm (HSA) is introduced. HSA is an evolutionary method which is inspired in musicians improvising new harmonies while playing. Different to ...
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In this paper, a multilevel thresholding (MT) algorithm based on the harmony search algorithm (HSA) is introduced. HSA is an evolutionary method which is inspired in musicians improvising new harmonies while playing. Different to other evolutionary algorithms, HSA exhibits interesting search capabilities still keeping a low computational overhead. The proposed algorithm encodes random samples from a feasible search space inside the image histogram as candidate solutions, whereas their quality is evaluated considering the objective functions that are employed by the Otsu's or Kapur's methods. Guided by these objective values, the set of candidate solutions are evolved through the HSA operators until an optimal solution is found. Experimental results demonstrate the high performance of the proposed method for the segmentation of digital images.
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A new spectral–spatial method for classification of hyperspectral images is introduced. The proposed approach is based on two segmentation methods, fractional-order Darwinian particle swarm optimization and mean shift segmentatio...
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A new spectral–spatial method for classification of hyperspectral images is introduced. The proposed approach is based on two segmentation methods, fractional-order Darwinian particle swarm optimization and mean shift segmentation. The output of these two methods is classified by support vector machines. Experimental results indicate that the integration of the two segmentation methods can overcome the drawbacks of each other and increase the overall accuracy in classification.
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The modified two-stage multithreshold Otsu (TSMO) method based on a two-stage Otsu optimization approach is proposed for multilevel thresholding. The proposed method yields the same set of thresholds as those obtained by using the...
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The modified two-stage multithreshold Otsu (TSMO) method based on a two-stage Otsu optimization approach is proposed for multilevel thresholding. The proposed method yields the same set of thresholds as those obtained by using the conventional Otsu method, but it greatly decreases the required computation time, especially for a large number of clusters. In addition, an effective method of histogram-based valley estimations is presented for determining an appropriate number of clusters for an image. Various real-world images were used to evaluate the performance of the proposed method. Experimental results show that the speed of computation for the proposed method is about 19000 times faster than that for the conventional Otsu method when the number of clusters is 7.
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Image segmentation has been widely used in document image analysis for extraction of printed characters, map processing in order to find lines, legends, and characters, topological features extraction for extraction of geographica...
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Image segmentation has been widely used in document image analysis for extraction of printed characters, map processing in order to find lines, legends, and characters, topological features extraction for extraction of geographical information, and quality inspection of materials where defective parts must be delineated among many other applications. In image analysis, the efficient segmentation of images into meaningful objects is important for classification and object recognition. This paper presents two novel methods for segmentation of images based on the Fractional-Order Darwinian Particle Swarm Optimization {FODPSO) and Darwinian Particle Swarm Optimization {DPSO) for determining the n-1 optimal n-level threshold on a given image. The efficiency of the proposed methods is compared with other well-known thresholding segmentation methods. Experimental results show that the proposed methods perform better than other methods when considering a number of different measures.
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A brain tumor is an abnormal growth of tissues inside the skull that can interfere with the normal functioning of the neurological system and the body, and it is responsible for the deaths of many individuals every year. Magnetic ...
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A brain tumor is an abnormal growth of tissues inside the skull that can interfere with the normal functioning of the neurological system and the body, and it is responsible for the deaths of many individuals every year. Magnetic Resonance Imaging (MRI) techniques are widely used for detection of brain cancers. Segmentation of brain MRI is a foundational process with numerous clinical applications in neurology, including quantitative analysis, operational planning, and functional imaging. The segmentation process classifies the pixel values of the image into different groups based on the intensity levels of the pixels and a selected threshold value. The quality of the medical image segmentation extensively depends on the method which selects the threshold values of the image for the segmentation process. The traditional multilevel thresholding methods are computationally expensive since these methods thoroughly search for the best threshold values to maximize the accuracy of the segmentation process. Metaheuristic optimization algorithms are widely used for solving such problems. However, these algorithms suffer from the problem of local optima stagnation and slow convergence speed. In this work, the original Bald Eagle Search (BES) algorithm problems are resolved in the proposed Dynamic Opposite Bald Eagle Search (DOBES) algorithm by employing Dynamic Opposition Learning (DOL) at the initial, as well as exploitation, phases. Using the DOBES algorithm, a hybrid multilevel thresholding image segmentation approach has been developed for MRI image segmentation. The hybrid approach is divided into two phases. In the first phase, the proposed DOBES optimization algorithm is used for the multilevel thresholding. After the selection of the thresholds for the image segmentation, the morphological operations have been utilized in the second phase to remove the unwanted area present in the segmented image. The performance efficiency of the proposed DOBES based multilevel thresholding algorithm with respect to BES has been verified using the five benchmark images. The proposed DOBES based multilevel thresholding algorithm attains higher Peak Signal-to-Noise ratio (PSNR) and Structured Similarity Index Measure (SSIM) value in comparison to the BES algorithm for the benchmark images. Additionally, the proposed hybrid multilevel thresholding segmentation approach has been compared with the existing segmentation algorithms to validate its significance. The results show that the proposed algorithm performs better for tumor segmentation in MRI images as the SSIM value attained using the proposed hybrid segmentation approach is nearer to 1 when compared with ground truth images.
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In the present paper, a hybrid multilevel thresholding technique that combines intuitionistic fuzzy sets and tsallis entropy has been proposed for the automatic delineation of the tumor from magnetic resonance images having vague ...
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In the present paper, a hybrid multilevel thresholding technique that combines intuitionistic fuzzy sets and tsallis entropy has been proposed for the automatic delineation of the tumor from magnetic resonance images having vague boundaries and poor contrast. This novel technique takes into account both the image histogram and the uncertainty information for the computation of multiple thresholds. The benefit of the methodology is that it provides fast and improved segmentation for the complex tumorous images with imprecise gray levels. To further boost the computational speed, the mutation based particle swarm optimization is used that selects the most optimal threshold combination. The accuracy of the proposed segmentation approach has been validated on simulated, real low-grade glioma tumor volumes taken from MICCAI brain tumor segmentation (BRATS) challenge 2012 dataset and the clinical tumor images, so as to corroborate its generality and novelty. The designed technique achieves an average Dice overlap equal to 0.82010, 0.78610 and 0.94170 for three datasets. Further, a comparative analysis has also been made between the eight existing multilevel thresholding implementations so as to show the superiority of the designed technique. In comparison, the results indicate a mean improvement in Dice by an amount equal to 4.00% (p < 0.005), 9.60% (p < 0.005) and 3.58% (p < 0.005), respectively in contrast to the fuzzy tsallis approach.
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PurposeMicrofinance has become an important way to alleviate poverty. Though four decades have passed since its introduction, its impact is still not entirely clear. What makes it difficult to ascertain its efficacy is the existen...
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PurposeMicrofinance has become an important way to alleviate poverty. Though four decades have passed since its introduction, its impact is still not entirely clear. What makes it difficult to ascertain its efficacy is the existence of diverse types of microfinance organizations and client profiles. Microfinance institutions must primarily pay more attention to the client, and to the mechanism through which financial services are delivered. The purpose of this paper is to identify the profiles of microfinance customers and the features of their operations.Design/methodology/approachIn this paper, multilevel latent class models were estimated to reveal clusters of operations and classes of clients.FindingsThe results show that there are six clusters of operations and four classes of clients in the market, each with distinct profiles and needs. Different strategies are recommended for each cluster and class.Originality/valueNumerous studies have focused on the importance of getting to know the clients of microfinance programs, but none as yet have used market segmentation as a way to do so. The goal is to generate better strategies to help clients improve their business results. Applying market segmentation to the microfinance market may point to different products for different groups of clients, taking the real needs of each of them into account.
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In this article, we propose an automated segmentation system for liver tumors using magnetic resonance imaging and computed tomography. The proposed system is based on the algorithm of multilevel thresholding with electromagnetism...
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In this article, we propose an automated segmentation system for liver tumors using magnetic resonance imaging and computed tomography. The proposed system is based on the algorithm of multilevel thresholding with electromagnetism optimization (EMO). The system starts with visualizing a patient's digital communication in medicine (DICOM) abdominal data set in three views. Two-stage active contour segmentation methods that integrate region-based local and global techniques using the active geodesic contour technique are proposed to segment the liver. To increase the accuracy and speed of segmentation for liver images, we identify the optimal threshold of the image segmentation method based on EMO with Otsu and Kapur algorithms. EMO offers interesting search capabilities while keeping a low computational cost. The proposed system was tested using a set of five DICOM data sets. All images were of the same size and stored in JPEG format (512 x 512 pixels). Experimental results illustrate that the proposed system outperforms state-of-the-art methods such as the watershed algorithm. The average sensitivity, specificity, and accuracy of the segmented liver using the active contour model were 97.05%, 99.88%, and 98.47%, respectively. Moreover, the average sensitivity, specificity, and accuracy of the segmented liver tumor results were 94.15%, 99.57%, and 96.86%, respectively.
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Segmentation is considered as one of the most significant tasks in image processing. It consists of separating the pixels into different segments based on their intensity level according to threshold values. Selecting the optimal ...
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Segmentation is considered as one of the most significant tasks in image processing. It consists of separating the pixels into different segments based on their intensity level according to threshold values. Selecting the optimal threshold value is the key to best quality segmentation. Multilevel thresholding (MT) is an essential approach for image segmentation, and it has become very popular during the past few years, but while increasing the level of thresholds, computational complexity also increases exponentially. In order to overcome this drawback, several metaheuristics-based algorithms have been used for determining the optimal MT levels. Learning enthusiasm-based teaching-learning-based optimization (LebTLBO) is a recently developed efficient, simple-to-implement and computationally inexpensive algorithm. It simulates the behaviors of the teaching and learning process in a classroom and gives the probability of getting the amount of information by the learner (student) from the educator. In this paper, LebTLBO is applied on ten standard test images having a diverse histogram, which are taken from Berkeley Segmentation Dataset 500 (BSDS500) (Martin et al. in a database of human segmented natural images and its application to evaluate segmentation algorithms and measure ecological statistics, 2001) benchmark image set for segmentation. The search capability of the algorithm is combined with Otsu and Kapur's entropy MT objective functions for image segmentation. The proposed approach is compared with the existing state-of-the-art optimization algorithms such as MTEMO, GA, PSO and BF for both Otsu and Kapur's entropy methods. Qualitative experimental outcomes demonstrate that LebTLBO is highly efficient in terms of performance metrics such as PSNR, mean, threshold values, number of iterations taken to converge and image segmentation quality.
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