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Figure 2. Overall process of the system -An Optimized Clustering Approach for Automated Detection of White Matter Lesions in MRI Brain Images

This paper mainly focuses on automated detection of White Matter Lesions of brain using fast and efficient clustering algorithms. The goal of clustering a medical image is to simplify the representation of an image into a meaningful image and makes it easier to analyze. As a first step, MRI brain image is pre-processed using Contrast Stretching technique which is one of the efficient image enhancement techniques. The pre-processed image is subjected to clustering. The clustering algorithms include Fuzzy c-means Clustering (FCM), Geostatistical Possibilistic Clustering (GPC) and Geostatistical Fuzzy Clustering Model (GFCM). However clustering techniques are sensitive to initialization and are easily trapped in local optima. In order to obtain an optimized result, the clustered images are undergone optimization. Particle swarm optimization (PSO) is a stochastic global optimization tool which is used in many optimization problems. Figure 2 represents overall process of automatic detection of WMLs of brain. Since MS lesions present different characteristics from lesions in elderly individuals there are many clustering models to determine the accuracy but those methods are not directly applicable to predict the accurate lesions because of the decreased contrast between White Matter and Grey Matter in elderly people. The proposed clustering models are derived by extending the objective functions of FCM and Possibilistic clustering with a Geostatistical (spatial) model. These algorithms are applied to real magnetic resonance images and is shown to be more robust to noise and other artifacts than competing approaches.

Figure 9. Performance analysis of FCM-PSO, GPC-PSO and GFCM-PSO-An Optimized Clustering Approach for Automated Detection of White Matter Lesions in MRI Brain Images

Figure 8. Performance analysis of FCM, GPC and GFCM Figure 9.-An Optimized Clustering Approach for Automated Detection of White Matter Lesions in MRI Brain Images

All scans obtained from different image clustering models are manually ranked based on values in table 1. Table 2 represents WML detection rates of optimized images. FCM, GPC and GFCM clustering methods and hybrid optimized methods (FCM-PSO, GPC-PSO and GFCM-PSO) are applied on a dataset of 208 images and ranking is done in terms of under detected, over detected, properly detected as shown in figure 8 and figure 9. The number of images detected properly in GFCM is comparatively high than FCM and GPC. The optimized result of GFMC provides accurate detection of WMLs and it properly detects 195 images.

Figures -using Particle Swarm Optimization (PSO)-An Optimized Clustering Approach for Automated Detection of White Matter Lesions in MRI Brain Images

The performance of WML quantification is evaluated using clustering algorithms. When the image is pre-processed, contrast of the image is enhanced. The resulting enhanced image is clustered using the effective clustering algorithms. Figure 3 represents the input image for WML detection. In order to increase robustness, the noisy medical image is pre-processed. Figure 4 depicts the pre-processed image. Bright contrast stretching, which is one of the image enhancement (pre-processing) techniques is applied. After pre-processing the enhanced image is subjected to clustering. Three clustering models are proposed to provide accurate results. All scans obtained from different image clustering models are manually ranked based on values in table 1. Table 2 represents WML detection rates of optimized images. FCM, GPC and GFCM clustering methods and hybrid optimized methods (FCM-PSO, GPC-PSO and GFCM-PSO) are applied on a dataset of 208 images and ranking is done in terms of under detected, over detected, properly detected as shown in figure 8 and figure 9. The number of images detected properly in GFCM is comparatively high than FCM and GPC.

Figures a, b ,c -8-An Optimized Clustering Approach for Automated Detection of White Matter Lesions in MRI Brain Images

In turn, the optimized results of GFCM provide overall accuracy of 95%. Figures 8(a), 8(b) and 8(c) shows the comparison results of clustering models and optimization technique in terms of Se, Sp and Acc.

opencc-by-4.0Dec 2014View details →

BRAIN Journal-On the Idea of a New Artificial Intelligence Based Optimization Algorithm Inspired From the Nature of Vortex-Figure 1. Working mechanism of the VOA.

As it can be seen from the algorithm steps, the VOA employs simple equations. It is an advantage that the algorithm can be formed and applied within optimization problems whereas alternative algorithms may contain some complex solution steps (This situation may be also an disadvantage for the VOA when it is applied in more difficult optimization problems but while the world is transformed into a ‘strong simplicity’, the VOA may be a practical solution approach). The working mechanism of the VOA can be visualized briefly as like in Figure 1.

Figure 6. (a1), (a2), (a3), (a4), (a5), (a6), (a7) and (a8) watermarked image is degraded respectively through JPEG2000 compression, JPEG compression, median filtering, adding Salt&Pepper noise, rotating, center cropping, surrounding cropping and scaling. (b1), (b2), (b3), (b4), (b5), (b6), (b7) and (b8) The corresponding extracted watermarks.-Discrete Wavelet Transform Method: A New Optimized Robust Digital Image Watermarking Scheme

This paper has described a scheme for digital watermarking of still images based on discrete wavelet transform. In the proposed method, the embedded logo watermark can be extracted without access to the original image. It has been confirmed that the proposed watermarking method is able to extract the embedded logo watermark from the watermarked images that have degraded through compression, filtering, cropping and scaling. Although this algorithm is not robust against rotation, it can completely extract the watermark from watermarked images that lose about 35% of their areas by cropping attack.

Figure 1. (a) Original watermark (b) extracted watermarks after compression(c) merged watermark-Discrete Wavelet Transform Method: A New Optimized Robust Digital Image Watermarking Scheme

Therefore, each bit of the logo watermark is stored in one coefficient of a sub-block to keep the capacity of watermarking fixed. When a region of the watermarked image is destroyed; the whole watermark can be extracted using other regions of the watermarked image by merging extracted watermarks. Figure 1 shows result of merging logo watermarks that were extracted from a compressed (with JPEG2000 algorithm) watermarked image.

Figure 4. (a) The original "Hookah" image (b) Watermarked "Hookah" with Q=35 (c) The original "Baby" image (d) Watermarked "Baby" with Q=35-Discrete Wavelet Transform Method: A New Optimized Robust Digital Image Watermarking Scheme

A set of distortions is applied to the watermarked image and the watermark is extracted from the distorted image. We used bit correct rate (BCR) to evaluate our proposed algorithm and it is calculated from the following equation [6].

Figure 2. LL2 sub-band is divided into sub-block-Discrete Wavelet Transform Method: A New Optimized Robust Digital Image Watermarking Scheme

In the following experiments, two gray-level images with size of 512 by 512, “Baby” and “Hookah” are the test images. The binary image “IAU” with size of 32 by 32 is used in our simulations as a watermark. Figure 3 shows the watermark. In the experiments Haar wavelet filter was used for discrete wavelet transform. The level of wavelet decomposition (n) and the number of sub-blocks (K) were also assumed to be 2 and 16 respectively. The proposed watermarking algorithm is evaluated from the point view of embedded watermark transparency and robustness; the result of each is shown in next two sections.

opencc-by-4.0Aug 2015View details →

Figure 1.Flowchart of the CoDOA.-Realizing an Optimization Approach Inspired from Piaget's Theory on Cognitive Development

The objective of this paper is to introduce an artificial intelligence based optimization approach, which is inspired from Piaget’s theory on cognitive development. The approach has been designed according to essential processes that an individual may experience while learning something new or improving his / her knowledge. These processes are associated with the Piaget’s ideas on an individual’s cognitive development. The approach expressed in this paper is a simple algorithm employing swarm intelligence oriented tasks in order to overcome single-objective optimization problems. For evaluating effectiveness of this early version of the algorithm, test operations have been done via some benchmark functions. The obtained results show that the approach / algorithm can be an alternative to the literature in terms of single-objective optimization. The authors have suggested the name: Cognitive Development Optimization Algorithm (CoDOA) for the related intelligent optimization approach.

opencc-by-4.0Jan 2016View details →

Figure 4. A graphic on values of TP, TN, FP, and FN for each different application process.-Cognitive Development Optimization Algorithm Based Support Vector Machines for Determining Diabetes

This study has proposed a diabetes diagnosis system, which is formed via both Support Vector Machines (SVM) and Cognitive Development Optimization Algorithm (CoDOA). In this approach, the training process of the SVM has been supported with the CoDOA and after determining the most optimum sigma (σ) parameter of the Gauss (RBF) kernel function (so the most optimum SVM), a better classification formation has been tried to be achieved. In the context of the study, diabetes data set, which is related to Pima Indians, has been used for evaluating effectiveness of the proposed approach and after six different application processes, it was seen that the approach is well-enough on classification, which means being capable of determining diabetes. There are also some future works regarding the developed CoDOA-SVM based approach. In this context, there will be some more works for improving classification accuracy and also setting different optimization plans on i.e. different parameters of the kernel function. Additionally, it is aimed to evaluate the approach with datasets belonging to different diseases.

opencc-by-4.0Jan 2016View details →

Figure 3. A brief schema of the CoDOA-SVM approach-Cognitive Development Optimization Algorithm Based Support Vector Machines for Determining Diabetes

In the Equation 23, TP stands for true classified diabetes positive individuals; TN stands for true classified diabetes negative individuals; FP stands for false classified diabetes positive individuals and finally, FN stands for false classified diabetes negative individuals. • After determining good (optimum) particles, default CoDOA steps are run. • After achieving the total iteration number, it is allowed to train the SVM via optimum Gauss (RBF) kernel function parameters, by using the optimum particle value [sigma (σ) value]. • The trained SVM is now ready for the classification and so is diabetes determination process. A brief schema of the CoDOA-SVM approach is also provided in Figure 3.

opencc-by-4.0Jan 2016View details →

Figure 1. Maximum margin hyper-plane (Cortes & Vapnik, 1995).-Cognitive Development Optimization Algorithm Based Support Vector Machines for Determining Diabetes

In other words, it aims to find the state in which the distance between the two classes is the maximum. The hallmarks of this classification reasoning are the support vectors chosen from the training set, and they are located on the closest points of both classes (Javed, Ayyaz, & Mehmood, 2007). In Figure 1, an example of support vectors and a maximum margin hyper-plane (in other words, an optimum separating hyper- plane) is shown (Cortes & Vapnik, 1995).

opencc-by-4.0Feb 2018View details →

ENTICE Multi-objective optimization framework synthetic evaluation data-sets

This dataset contains synthetic usage data for evaluation of the multi-objective redistribution framework for distributed VMI repositories.  The data is stored in a Java object and it should be directly loaded.

opencc-by-4.0Mar 2018View details →

Dataset for "Resonance with subthreshold oscillatory drive organizes activity and optimizes learning in neural networks"

This is the experimental dataset for the paper "Resonance with subthreshold oscillatory drive organizes activity and optimizes learning in neural networks", doi/10.1073/pnas.1716933115.

opencc-by-4.0Mar 2018View details →

Optimal modes for wavefront sensorless adaptive optics. Turbulence- and oocyte-induced phase screens and Mathematica notebooks.

This notebook and phase screens constitute a numerical experiment to calculate the error of the wavefront approximation using first N modes of the  a) Zernike and b) Lukosz-Braat polynomials, c) SVD modes obtained with respect to the gradient-dot product, and the d) eigenfunctions of the Laplace operator with the Neumann boundary conditions. It's a complementary material to a paper submitted to Optics Express.

opencc-by-4.0Nov 2017View details →

Optimization and improvement of output performance in G-band extended interaction klystron

We optimized and improved the output performance of G-band Extended Interaction Klystron, including the optimized period, influence of gap voltage on the beam modulation, and optimized gap length as well as non-uniform gaps are analyzed, and these figures are the simulation results with .tif format.

opencc-by-4.0Apr 2018View details →

Using the Genetic Algorithm for the Optimization of Dynamic School Bus Routing Problem-Figure 9. Step by step drawing of the selected route solution

When the solution that is desired to be viewed is double clicked, the connections between bus stops are drawn in turn, and the route is shown as can be seen in Figure 9.