In this study, we propose an inverse solution algorithm through which both the aquifer parameters and the zone structure of these parameters can be determined based on a given set of observations on piezometric heads....
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In this study, we propose an inverse solution algorithm through which both the aquifer parameters and the zone structure of these parameters can be determined based on a given set of observations on piezometric heads. In the zone structure identification problem, kernel-basedfuzzyc-means (KFcM) clustering method is used. The association of the zone structure with the transmissivity distribution is accomplished through a coupled simulation-optimization model. In the optimization model, genetic algorithm (GA) is used due to its efficiency in finding global or near global optimum solutions. Since the solution is based on the GA procedures, the optimization process starts with a randomly generated initial solution. Thus, there is no need to define an initial estimate of the solution. This is an advantage when compared to other studies reported in the literature. Further, the objective function used in the optimization model does not include a reference to field transmissivity data, which is another advantage of the proposed methodology. Numerical examples are provided to demonstrate the performance of the proposed algorithm. In the first example, transmissivity values and zone structures are determined for a known number of zones in the solution domain. In the second example, optimum number of zones as well as the transmissivity values and the zone structures are determined iteratively. A sensitivity analysis is also performed to test the performance of the proposed solution algorithm based on the number of observation data necessary to solve the problem accuratety. Numerical results indicate that the proposed algorithm is effective and efficient and may be used in the inverse parameter estimation problems when both parameter values and zone structure are unknown. (c) 2007 Elsevier B.V. All rights reserved.
The brain tumors are formed when groups of abnormal cells develop in the brain and possess the capacity to infiltrate nearby tissues. The early detection is necessary to aid doctors in treating cancer patients to incr...
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The application of image processing in medical image analysis offers physicians numerous advantages in diagnosing and predicting patient recovery. A typical task that requires sensitive handling is the detection of a ...
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The application of image processing in medical image analysis offers physicians numerous advantages in diagnosing and predicting patient recovery. A typical task that requires sensitive handling is the detection of a cerebral aneurysm, which generally involves the arteries in the human brain. Due to peculiar and complicated tissue structures, such as white and grey matter, which make up most of the brain, it is indeed challenging to visualize the aneurysm and the affected parts. Magnetic resonance angiography (MRA) is a tool for such a process, and it is difficult for radiologists and oncologists to decide. To overcome these drawbacks, the researchers proposed a novel algorithm named artificial bee colony optimization (ABc) with spatially constrained adaptively regularized kernel function-basedfuzzyc-means (ABc-ScARKFcM) in this work. The system outperforms the conventional fuzzyc-meansclustering method (FcM), which has inaccuracies in intensity handling and segmentation, and a poor convergence rate. The developed algorithm performed well on clinical MRA and Magnetic Resonance images (MRI) from the BraTS challenge dataset (2013, 2015, 2018, 2019, 2020 and 2021). The algorithm achieved dice score, sensitivity and specificity of 87.89%, 98.9% and 98.98%, respectively, which is very remarkable and shows that the applicability of the algorithm can be extended to oncology applications, where suppression of openness/anonymity is expected in diagnosis and assessment of prognosis of patients after therapy.
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