Sexual Function in Male Patients with Metabolic Syndrome and Effective Parameters on Erectile Dysfunction

Purpose: We aimed to investigate the relationship between metabolic syndrome and sexual function and effective parameters on erectile dysfunction (ED). Materials and Methods: A total of 1300 individuals were included in this study between January 2009 and July 2012. All of individuals were asked to fill in an International Index for Erectile Function (IIEF) questionnaire. The presence of metabolic syndrome was determined when any three or more of the five risk factors were present according to the National Cholesterol Education Program (NCEP) Adult Treatment Panel (ATP)-III. Obese individuals were divided into six groups according to modified World Health Organization (WHO) definition. Effective parameters on erectile dysfunction were investigated in individuals with metabolic syndrome. Results: Metabolic syndrome was detected in 455 individuals (35%). Mean domain scores of IIEF for all parameters were higher in individuals without metabolic syndrome than individuals with metabolic syndrome (p < 0.05). Mean domain scores of IIEF were lower in individuals with class 3 obesity than individuals with other obese groups (p < 0.05) for erectile dysfunction. There was statistical difference in terms of mean score of IIEF-Erectile function between smoking and nonsmoking groups (p < 0.05). Seventy percent of individuals with metabolic syndrome and 45% of individuals without metabolic syndrome had ED (p < 0.001). Logistic regression analysis revealed that waist circumference (WC) was the most important criteria for ED (p < 0.05). Conclusions: Metabolic syndrome, smoking and obesity seem to be potential risk factors for ED. We recommend individuals with metabolic syndrome, smoking and obesity should be questioned about ED

A Fuzzy Inference System Combined with Wavelet Transform for Breast Mass Classification

This paper proposes a combination of the Fast Wavelet Transform (FWT) and Adaptive Neuro-fuzzy Inference System (ANFIS) methods. The goal is classification of breast masses as benign or malignant by applying this method consecutively to the extracted features of the Region of Interests (ROIs). This study is developed to decrease the number of the missing cancerous regions or unnecessary biopsies. The neurofuzzy subtractive clustering classification method achieved a classification accuracy of 85% without using FWT multiresolution analysis and 92% with FWT. The satisfying results demonstrate that the developed system could help the radiologists for a true diagnosis

A Wavelet-Based Mammographic Image Denoising and Enhancement with Homomorphic Filtering

Breast cancer continues to be a significant public health problem in the world. The diagnosing mammography method is the most effective technology for early detection of the breast cancer. However, in some cases, it is difficult for radiologists to detect the typical diagnostic signs, such as masses and microcalcifications on the mammograms. This paper describes a new method for mammographic image enhancement and denoising based on wavelet transform and homomorphic filtering. The mammograms are acquired from the Faculty of Medicine of the University of Akdeniz and the University of Istanbul in Turkey. Firstly wavelet transform of the mammograms is obtained and the approximation coefficients are filtered by homomorphic filter. Then the detail coefficients of the wavelet associated with noise and edges are modeled by Gaussian and Laplacian variables, respectively. The considered coefficients are compressed and enhanced using these variables with a shrinkage function. Finally using a proposed adaptive thresholding the fine details of the mammograms are retained and the noise is suppressed. The preliminary results of our work indicate that this method provides much more visibility for the suspicious regions

Mammographical mass detection and classification using Local Seed Region Growing-Spherical Wavelet Transform (LSRG-SWT) hybrid scheme

The purpose of this study is to implement accurate methods of detection and classification of benign and malignant breast masses in mammograms. Our new proposed method, which can be used as a diagnostic tool, is denoted Local Seed Region Growing-Spherical Wavelet Transform (LSRG-SWT), and consists of four steps. The first step is homomorphic filtering for enhancement, and the second is detection of the region of interests (ROIs) using a Local Seed Region Growing (LSRG) algorithm, which we developed. The third step incoporates Spherical Wavelet Transform (SWT) and feature extraction. Finally the fourth step is classification, which consists of two sequential components: the 1st classification distinguishes the ROIs as either mass or non-mass and the 2nd classification distinguishes the masses as either benign or malignant using a Support Vector Machine (SVM). The mammograms used in this study were acquired from the hospital of Istanbul University (I.U.) in Turkey and the Mammographic Image Analysis Society (MIAS). The results demonstrate that the proposed scheme LSRG-SWT achieves 96% and 93.59% accuracy in mass/non-mass classification (1st component) and benign/malignant classification (2nd component) respectively when using the I.U. database with k-fold cross validation. The system achieves 94% and 91.67% accuracy in mass/non-mass classification and benign/malignant classification respectively when using the I.U. database as a training set and the MIAS database as a test set with external validation. (C) 2013 Elsevier Ltd. All rights reserved

Computer-aided classification of breast masses in mammogram images based on spherical wavelet transform and support vector machines

Breast cancer can be effectively detected and diagnosed using the technology of digital mammography. However, although this technology has been rapidly developing recently, suspicious regions cannot be detected in some cases by radiologists, because of the noise or inappropriate mammogram contrast. This study presents a classification of segmented region of interests (ROIs) as either benign or malignant to serve as a second eye of the radiologists. Our study consists of three steps. In the first step, spherical wavelet transform (SWT) is applied to the original ROIs. In the second step, shape, boundary and grey level based features of wavelet (detail) and scaling (approximation) coefficients are extracted. Finally, in the third step, malignant/benign classification of the masses is implemented by giving the feature matrices to a support vector machine system. The proposed system achieves 91.4% and 90.1% classification accuracy using the dataset acquired from the hospital of Istanbul University in Turkey and the free Mammographic Image Analysis Society, respectively. Furthermore, discrete wavelet transform, which produces 83.3% classification accuracy, is applied to the coefficients to make a comparison with the SWT method

Multifont Ottoman character recognition using Support Vector Machine

In this study, an Optical Character Recognition (OCR) system, which implements segmentation, normalization, edge detection and recognition of the Ottoman script, is proposed. Each multifont Ottoman character is written with four different shapes according to its position in the word being at beginning, middle, at the end and in isolated form. We have used printed type of Ottoman scripts in image acquisition. Then image segmentation, normalization and finally edge detection are performed for feature extraction, where edge detection is achieved by Cellular Neural Network (CNN) approach. After these pre-proces steps, we recognize these multifont Ottoman characters using Support Vector Machine (SVM) technique. In SVM training, polynomial (linear and quadratic) and Gaussian Radial Basis Function kernels are chosen. The proposed recognition system has succeeded in classification up to 87.32% with quadratic kernel

Mammographic Mass Detection using Wavelets as Input to Neural Networks

The objective of this paper is to demonstrate the utility of artificial neural networks, in combination with wavelet transforms for the detection of mammogram masses as malign or benign. A total of 45 patients who had breast masses in their mammography were enrolled in the study. The neural network was trained on the wavelet based feature vectors extracted from the mammogram masses for both benign and malign data. Therefore, in this study, Multilayer ANN was trained with the Backpropagation, Conjugate Gradient and Levenberg-Marquardt algorithms and ten-fold cross validation procedure was used. A satisfying sensitivity percentage of 89.2% was achieved with Levenberg-Marquardt algorithm. Since, this algorithm combines the best features of the Gauss-Newton technique and the other steepest-descent algorithms and thus it reaches desired results very fast

A BACKPROPAGATION NEURAL NETWORK APPROACH FOR OTTOMAN CHARACTER RECOGNITION

The Ottoman Empire established in 1299 and continued 6 centuries covering an area of about 5.6 million squared km. The Empire left a large collection of valuable archives interesting to historians from all over the world. Investigation and understanding these documents will shed light on the history of the world. In order to achieve access of the considered information by worldwide scientists, it is essential to translate Ottoman characters into Latin alphabet. Thus, we aimed to recognize the Ottoman characters using Artificial Neural Network (ANNT) and compared it with Support Vector Machine (SVM) approaches. We used printed type of Ottoman scripts in image acquisition. Pre-processing such as normalization and edge detection were implemented. Multilayer perceptions of ANN were trained using the backpropagation learning algorithm. As a result of our research, we are able to classify the Ottoman characters with 85.5% classification accuracy using the proposed recognition system

Can preoperative neutrophil lymphocyte ratio predict malignancy in patients undergoing partial nephrectomy because of renal mass?

ABSTRACT Purpose: To evaluate the importance of preoperative neutrophil lymphocyte ratio (NLR) predicting malignancy in patients who undergo partial nephrectomy due to renal mass. Materials and Methods: Seventy nine patients who underwent open partial nephrectomy for renal masses were included in this retrospective study. In preoperative routine blood tests, renal ultrasonography and contrast-enhanced computed tomography were performed for all patients. Preoperative neutrophil lymphocyte ratio were compared in patients with clear cell renal cell carcinoma (Group1, 65 patients) and benign lesions (Group 2, 14 patients). The predictive ability of NLR was analyzed by ROC curves and Youden Index method was used to identify the cut-off value for NLR. Results: The mean age of patients was 59.8±11.7 years in Group1 and 57.4±12.6 years in Group 2 (p=0.493). The mean tumor size was 3.8±1.2 cm in Group 1 and 3.3±1.0 cm in Group 2 (p=0.07). The median NLR was 2.48 (1.04) in Group 1 and 1.63 (0.96) in Group 2 (p<0.001). The area under a ROC curve was 0.799 (p<0.001). Conclusions: Preoperative neutrophil lymphocyte ratio may predict renal masses that can not be distinguished radiologically. Our results must be confirmed by large and properly designed prospective, randomized trials