This paper focuses on the issue of extracting retina vessels with supervised approach. Since the green channel in the retina image has the best contrast between vessel and non-vessel, this channel is used to separate vessels. In our approach we are proposing a technique of using gray-level co-occurrence matrix method for composition of the retinal images. It is based on fact that the co-occurrence matrix of retina image describes the transition of intensities between neighbour pixels, indicating spatial structural information of retina image. So, we first extract the features vector based on specified characteristics of the gray-level co-occurrence matrix and then we use these features vector to train a neural network approach for the classification method which makes our proposed approach more effective. Obtained results from the experiments in DRIVE and STARE database shows the advantage of the proposed method in contrast to current methods. This advantage is evaluated by the criteria of sensitivity, specificity, area under ROC and accuracy. The result of such a conversion as the input vector of a multilayer perceptron neural network will be trained and tested. Although in recent years different methods have been presented in this respect, but results of simulation shows that the proposed algorithm has a very high efficiency than the other researches.
There are various methods and algorithms to detect the optic discs in retinal images. In recent years, much attention has been given to the utilization of the intelligent algorithms. In this paper, we present a new automated method of optic disc detection in human retinal images using the firefly algorithm. The firefly intelligent algorithm is an emerging intelligent algorithm that was inspired by the social behavior of fireflies. The population in this algorithm includes the fireflies, each of which has a specific rate of lighting or fitness. In this method, the insects are compared two by two, and the less attractive insects can be observed to move toward the more attractive insects. Finally, one of the insects is selected as the most attractive, and this insect presents the optimum response to the problem in question. Here, we used the light intensity of the pixels of the retinal image pixels instead of firefly lightings. The movement of these insects due to local fluctuations produces different light intensity values in the images. Because the optic disc is the brightest area in the retinal images, all of the insects move toward brightest area and thus specify the location of the optic disc in the image. The results of implementation show that proposed algorithm could acquire an accuracy rate of 100 % in DRIVE dataset, 95 % in STARE dataset, and 94.38 % in DiaRetDB1 dataset. The results of implementation reveal high capability and accuracy of proposed algorithm in the detection of the optic disc from retinal images. Also, recorded required time for the detection of the optic disc in these images is 2.13 s for DRIVE dataset, 2.81 s for STARE dataset, and 3.52 s for DiaRetDB1 dataset accordingly. These time values are average value.
Abstract:The occurrence rates of melanoma are rising rapidly, which are resulting in higher death rates. However, if the melanoma is diagnosed in Phase I, the survival rates increase. The segmentation of the melanoma is one of the largest tasks to undertake and achieve when considering both beneath and over the segmentation. In this work, a new approach based on the artificial bee colony (ABC) algorithm is proposed for the detection of melanoma from digital images. This method is simple, fast, flexible, and requires fewer parameters compared with other algorithms. The proposed approach is applied on the PH2, ISBI 2016 challenge, the ISBI 2017 challenge, and Dermis datasets. These bases contained images are affected by different abnormalities. The formation of the databases consists of images collected from different sources; they are bases with different types of resolution, lighting, etc., so in the first step, the noise was removed from the images by using morphological filtering. In the next step, the ABC algorithm is used to find the optimum threshold value for the melanoma detection. The proposed approach achieved good results in the conditions of high specificity. The experimental results suggest that the proposed method accomplished higher performance compared to the ground truth images supported by a Dermatologist. For the melanoma detection, the method achieved an average accuracy and Jaccard's coefficient in the range of 95. 24-97.61%, and 83.56-85.25% in these four databases. To show the robustness of this work, the results were compared to existing methods in the literature for melanoma detection. High values for estimation performance confirmed that the proposed melanoma detection is better than other algorithms, which demonstrates the highly differential power of the newly introduced features.
In the practical reality of face recognition applications, the human face can have only a limited number of training images. However, it is known that, in general, increasing the number of training images also increases the performance of face recognition systems. In this case, a new set of training samples can be generated from the original samples, using the symmetry property of the face. Although many face recognition methods have been proposed in the literature, a robust face recognition system is still a challenging task. In this paper, recognition performance was improved by using the property of face symmetry. Moreover, the effects of illumination and pose variations were reduced. A Two-Dimensional Discrete Wavelet Transform, based on the Local Binary Pattern, which is a new approach for face recognition using symmetry, has been presented. The method has three main stages, preprocessing, feature extraction, and classification. A Two-Dimensional Discrete Wavelet Transform with Single-Level and Gaussian Low-Pass Filter were used, separately, for preprocessing. The Local Binary Pattern, Gray Level Co-Occurrence Matrix, and the Gabor filter were used for feature extraction, and the Euclidean Distance was used for classification. The proposed method was implemented and evaluated using the Olivetti Research Laboratory (ORL) and Yale datasets. This study also examined the importance of the preprocessing stage in a face recognition system. The experimental results showed that the proposed method had a recognition accuracy of 100%, for both the ORL and Yale datasets, and these recognition rates were higher than the methods in the literature.
Presently, climate change and global warming are the most uncontrolled global challenges due to the extensive fossil fuel usage for power generation and transportation. Nowadays, most of the developed countries are concentrating on developing alternative resources; consequently, they did huge investments in research and development. In general, alternative energy resources including hydropower, solar power, and wind energy are not harmful to nature. Today, solar power and wind power are very popular alternative energy sources due to their enormous availability in nature. In this paper, the photovoltaic cell and wind energy systems are investigated under various weather conditions. Based on the findings, we developed an advanced intelligent controller system that tracks the maximum power point. The MPPT controller is a must for the renewable energy sources due to unpredictable weather conditions. The main objective of this paper is to propose a new algorithm that is based on deep neural network (DNN) and maximum power point tracking (MPPT), which was simulated in a MATLAB environment for photovoltaic (PV) and wind-based power generation systems. The development of an advanced DNN controller that improves the power quality and reduces THD value for the microgrid integration of hybrid PV/wind energy system was performed. The MATLAB simulation tool has been used to develop the proposed system and tested its performance in different operating situations. Finally, we analyzed the simulation results applying the IEEE 1547 standard.
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