Over the last two decades, radiologists have been using multi-view images to detect tumors. Computer Tomography (CT) imaging is considered as one of the reliable imaging techniques. Many medical-image-processing techniques have been developed to diagnoses lung cancer at early or later stages through CT images; however, it is still a big challenge to improve the accuracy and sensitivity of the algorithms. In this paper, we propose an algorithm based on image fusion for lung segmentation to optimize lung cancer diagnosis. The image fusion technique was developed through Laplacian Pyramid (LP) decomposition along with Adaptive Sparse Representation (ASR). The suggested fusion technique fragments medical images into different sizes using the LP. After that, the LP is used to fuse the four decomposed layers. For the evaluation purposes of the proposed technique, the Lungs Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) was used. The results showed that the Dice Similarity Coefficient (DSC) index of our proposed method was 0.9929, which is better than recently published results. Furthermore, the values of other evaluation parameters such as the sensitivity, specificity, and accuracy were 89%, 98% and 99%, respectively, which are also competitive with the recently published results.

This paper presents a pragmatic approach established on the hybridization of nature-inspired optimization algorithms and Bernstein Polynomials (BPs), achieving the optimum numeric solution for Nonlinear Optimal Control Problems (NOCPs) of dynamical systems. The approximated solution for NOCPs is obtained by the linear combination of BPs with unknown parameters. The unknown parameters are evaluated by the conversion of NOCP to an error minimization problem and the formulation of an objective function. The Fitness Dependent Optimizer (FDO) and Genetic Algorithm (GA) are used to solve the objective function, and subsequently the optimal values of unknown parameters and the optimum solution of NOCP are attained. The efficacy of the proposed technique is assessed on three real-world NOCPs, including Van der Pol (VDP) oscillator problem, Chemical Reactor Problem (CRP), and Continuous Stirred-Tank Chemical Reactor Problem (CSTCRP). The final results and statistical outcomes suggest that the proposed technique generates a better solution and surpasses the recently represented methods in the literature, which eventually verifies the efficiency and credibility of the recommended approach.

In this work a new thresholding function referred to as ’mixture model shrinkage’ (MMS) based on the minimization of a convex cost function is proposed. Normally, thresholding functions underestimate larger signal amplitudes during the de-noising process. The proposed model is a more flexible shrinkage function as it solves the underestimation problem to a greater extent and thus efficiently de-noises the signal without affecting signal amplitudes. The Expectation minimization (EM) algorithm is used to find the model parameters along with the majorization-minimization (MM) algorithm that minimize the monotonic cost function. The proposed model is then applied for de-noising group sparse signals and Shepp Logan phantom images. Our experimental study shows that MMS outclasses current thresholding functions and overlapping group shrinkage algorithm without results suffering from underestimation. Furthermore, the proposed model has the smallest Root Mean Square Error (RMSE) for de-noising group sparse signals.

In most of the applications, signals acquired from different sensors are composite and are corrupted by some noise. In the presence of noise, separation of composite signals into its components without losing information is quite challenging. Separation of signals becomes more difficult when only a few samples of the noisy undersampled composite signals are given. In this paper, we aim to find Dantzig selector with overcomplete dictionaries using Accelerated Proximal Gradient Algorithm (APGA) for recovery and separation of undersampled composite signals. We have successfully diagnosed leukemia disease using our model and compared it with Alternating Direction Method of Multipliers (ADMM). As a test case, we have also recovered Electrocardiogram (ECG) signal with great accuracy from its noisy version using this model along with Proximity Operator based Algorithm (POA) for comparison. With less computational complexity compared with ADMM and POA, APGA has a good clustering capability depicted from the leukemia diagnosis.