Palmprint recognition system is regarded as the reliable and accurate biometric identification system available. Viewed in the palmprint recognition system of biometric approaches, compared to other models because it is a new handheld biometric feature recognition system has recently attracted the attention of researchers. In this study, palmprint recognition system based on Gabor wavelet transform has been developed. Firstly, image coordinate system is defined to facilitate image alignment for feature extraction. Then, region of interest is cropped from the palmprint images. With developed system feature extracted of region of interest and given of Multi layer perception classifier.
In this study, a new and rapid hidden resource decomposition method has been proposed to determine noisy pixels by adopting the extreme learning machines (ELM) method.The goal of this method is not only to determine noisy pixels, but also to protect critical structural information that can be used for disease diagnosis.In order to facilitate the diagnosis and also the treatment of patients in medicine, two-dimensional (2-D) images were calculated tomography (CT) which is obtained using medical imaging techniques.Utilizing a large number of CT images, promising results have been obtained from these experiments.The proposed method has shown a significant improvement on mean squared error and peak signal-to-noise ratio.The experimental results indicate that the proposed method is statistically efficient, and it has a good performance with a high learning speed.In the experiments, the results demonstrated that remarkable successive rates were obtained through the ELM method.
A biometric system provides automatic identification of individuals based on a unique feature or characteristic of the individuals. Palmprint biometric system has an important place among biometric identification systems because of its advantages. In this study, Gray Level Co-Occurence Matrix based palmprint recognition system which provides successful results for tissue type of image identifying has been proposed. Firstly, image coordinate system has been defined to facilitate image alignment for feature extraction. Then, region of interest is cropped from the palmprint images. The properties of the interested region have been determined using the developed system and it has been given to the classifier for recognition.
Medical images are visualized by computer and processed to obtain larger, more organized, and three-dimensional (3D) images.Thus, significant images are provided.The processed data facilitate diagnosis and treatment in the medical fields.The 3D surface models of related areas are formed by using volumetric data obtained by employing medical imaging methods such as Magnetic Resonance (MR) and Computer Tomography (CT).The purpose of this study is to obtain 3D images from the two-dimensional CT slices.These slices are obtained from the existing medical imaging devices and transferred to the z space and a mesh structure is provided between them.In addition, we investigated 3D imaging techniques, visualization, basic data types, conversion into main graphical components, and imaging algorithms.At the phase of obtaining 3D images; the image processing methods such as surface and volume imaging techniques, smoothing, denoising, and segmentation were used.The complexity and efficiency properties of the imaging algorithms were investigated and image enhancement algorithms were utilized.
Malaria is a febrile illness caused by a parasite called plasmodium. This life-threatening disease is preventable and treatable if diagnosed early. The World Health Organization aims to reduce the global malaria incidence and death rates by at least 90% until 2030. This disease is diagnosed by visually analyzing red blood cells with a microscope by experienced radiologists. Therefore, this situation may be erroneous due to subjective interpretations. In this study, red blood cells were trained with deep learning–based convolutional neural networks to diagnose malaria, and thus, their deep features were obtained. These obtained features are also trained with autoencoder networks. Thus, the chi-square feature selection algorithm was used to obtain distinctive features. Finally, the unique feature set obtained is given as an introduction to machine learning algorithms, and then a unique diagnostic model is proposed. As a result, 100% accuracy rate was obtained. The results are promising for the diagnosis of malaria disease.
In this study, a computer vision system has been developed to separate the pollen grains of plants according to their taxonomic categories without the help of an expert person. Pollen grains have a complex three-dimensional structure however they can be distinguished from one to another with their specific features. In the research, for the classification of pollen images the local edge patterns (LEP) were used. The proposed system is consists of three stages. At first Stage, Sobel edge detection algorithm was applied to pollen images to obtained new images that have prominent structural features. At the second stage LEP features were obtained and at the last stage the classification process was performed by machine learning methods by LEP features. The 98.48% classification success were obtained by LEP features.
