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Introduction: Demographic studies of a disease can reveal the characteristics of that disease among a specific population and will help the physicians to achieve a more accurate perception about it.The demographic of Lichen PlanoPilaris (LPP) among the Iranian population is unknown. The aim of this study is to describe the clinical, demographic, and histopathologic findings of lichen planopilaris in the Iranian population.Materials and Methods: In this cross-sectional study, all the patients with Lichen planopilaris were referred to the dermatology clinic of Imam Khomeini hospital from 2013 to 2015. Lichen planopilaris can be diagnosed by collecting histological evidence, dermatological examination, and clinical diagnosis. Their demographic characteristics, drug histories, onset of disease, and family histories were obtained by written questionnaire. Additionally, this study employed SPSS v.20 as the statistical analysis software.Results: One hundred patients were enrolled in this study. With an average age of 47.11 years, 78% of the patients were female, and 50 of these were housewives. The patients included were often from Tehran with Fars ethnicity. Among these patients, 7 had alopecia areata skin disease, and 10 of them suffered from thyroid disease. Most of the histopathology samples collected from these biopsies revealed degeneration of the basal layer of the follicular structure, perifollicular fibrosis, inflammatory cells, and atrophy of the pilosebaceous structures.Conclusion: Both the age spectrum and the disease distribution of LPP among the Iranian population were very diverse when compared to previous studies. Moreover, this study helps the physicians to have a brighter vision about the main reason and cause of LPP spread among diverse Iranian Ethnicities.

Introduction: The demographic of Lichen PlanoPilaris (LPP) among the Iranian population is unknown. The aim of this study is to describe the clinical, demographic, and histopathologic findings of lichen planopilaris in the Iranian population. Method: In this cross-sectional study, all the patients with Lichen planopilaris were referred to the dermatology clinic of Imam Khomeini hospital from 2013 to 2015. Their demographic characteristics, drug histories, onset of disease, and family histories were obtained by written questionnaire. Additionally, this study employed SPSS v.20 as the statistical analysis software. Results: One hundred patients were enrolled in this study. With an average age of 47.11 years, 78% of the patients were female, and 50 of these were housewives. The patients included were often from Tehran with Fars ethnicity. Among these patients, 7 had alopecia areata skin disease, and 10 of them suffered from thyroid disease. Most of the histopathology samples collected from these biopsies revealed degeneration of the basal layer of the follicular structure, perifollicular fibrosis, inflammatory cells, and atrophy of the pilosebaceous structures. Conclusion: Both the age spectrum and the disease distribution of LPP among the Iranian population were very diverse when compared to previous studies.

Display OmittedThe block diagram of the proposed system. Occasionally certain areas in the retina can be questionable for physicians which can lead to wrong interpretations for patients.A method is proposed that introduces a higher ability of segmentation by employing Skeletonization and a threshold selection based on Fuzzy Entropy.By extracting indices of the human retina properly, physicians will be able to estimate pathological injuries with a higher confidence.The proposed approach is fast and outperforms over other previously competitive techniques.The proposed approach consists of two stages. First of all, the retinal vessels was preprocessed by the HSV space and Wiener Filter. Then, the segmentation level is implemented by using Adaptive Filter that employs optimum threshold based on Fuzzy Entropy and Skeleton algorithm. The analysis of retina blood vessels in clinics indices is one of the most efficient methods employed for diagnosing diseases such as diabetes, hypertension and arthrosclerosis. In this paper, an efficient algorithm is proposed that introduces a higher ability of segmentation by employing Skeletonization and a threshold selection based on Fuzzy Entropy. In the first step, the blurring noises caused by hand shakings during ophthalmoscopy and color photography imageries are removed by a designed Wieners filter. Then, in the second step, a basic extraction of the blood vessels from the retina based on an adaptive filtering is obtained. At the last step of the proposed method, an optimal threshold for discriminating main vessels of the retina from other parts of the tissue is achieved by employing fuzzy entropy. Finally, an assessment procedure based on four different measurement techniques in the terms of retinal fundus colors is established and applied to DRIVE and STARE database images. Due to the evaluation comparative results, the proposed extraction of retina blood vessels enables specialists to determine the progression stage of potential diseases, more accurate and in real-time mode.

Automatic grading systems based on histopathological slide images are applied to various types of cancers. To date, cancer scientists and researchers have conducted many experiments to find and evaluate new and innovative automatic cancer grading systems to accelerate their therapeutic diagnoses and ultimately to enable more efficient prognoses. The previously proposed automatic or computer-aided systems for breast cancer grading, including specializing mitosis counting, suffer from various shortcomings. The most important one is their low efficiency along with high complexity due to the huge amount of features. In this paper, three types of features with more flexibility and less complexity are employed. These features are: completed local binary pattern (CLBP) as textural features, statistical moment entropy (SME) and stiffness matrix (SM) as a mathematical model which includes geometric, morphometric and shape-based features. In the proposed automatic mitosis detection method, these three types of features are fused with each other. The SM feature comprises of characteristics which are to be extracted for reliable discrimination of mitosis objects from non-mitosis ones. The evaluations are applied over histology datasets A and H provided by the Mitos-ICPR2012 contest sponsors. Employing both a nonlinear radial basis function (RBF) kernel for support vector machine (SVM) and also random forest classifiers, leads to the best efficiencies among the other competitive methods which have been proposed in the past. The results are in the form of F-measure criterion which is a basis for bioinformatics assessments and evaluation.

This paper introduces a computer-assisted diagnosis (CAD) system for automatic mitosis detection from breast cancer histopathology slide images. In this system, a new approach for reducing the number of false positives is proposed based on Teaching-Learning-Based optimization (TLBO). The proposed CAD system is implemented on the histopathology slide images acquired by Aperio XT scanner (scanner A). In TLBO algorithm, the number of false positives (falsely detected nonmitosis candidates as mitosis ones) is defined as a cost function and, by minimizing it, many of nonmitosis candidates will be removed. Then some color and texture (textural) features such as those derived from cooccurrence and run-length matrices are extracted from the remaining candidates and finally mitotic cells are classified using a specific support vector machine (SVM) classifier. The simulation results have proven the claims about the high performance and efficiency of the proposed CAD system.

Histopathology slides are one of the most applicable resources for pathology studies. As observation of these kinds of slides even by skillful pathologists is a tedious and time-consuming activity, computerizing this procedure aids the experts to have faster analysis with more case studies per day. In this paper, an automatic mitosis detection system (AMDS) for breast cancer histopathological slide images is proposed. In the proposed AMDS, the general phases of an automatic image based analyzer are considered and in each phase, some special innovations are employed. In the pre-processing step to segment the input digital histopathology images more precisely, 2D anisotropic diffusion filters are applied to them. In the training segmentation phase, the histopathological slide images are segmented based on RGB contents of their pixels using maximum likelihood estimation. Then, the mitosis and non-mitosis candidates are processed and hence that their completed local binary patterns are extracted object-wise. For the classification phase, two subsequently non-linear support vector machine classifiers are trained pixel-wise and object-wise, respectively. For the evaluation of the proposed AMDS, some object and region based measures are employed. Having computed the evaluation criteria, our proposed method performs more efficient according to f-measure metric (70.94% for Aperio XT scanner images and 70.11% for Hamamatsu images) than the methods proposed by other participants at Mitos-ICPR2012 contest in breast cancer histopathological images. The experimental results show the higher performance of the proposed AMDS compared with other competitive systems proposed in Mitos-ICPR2012 contest.

 Abstract—Automatic and reliable extraction of the minutiae from fingerprint images is a critical process in fingerprint matching and a main preprocess for this stage is Thinning. There are a lot of algorithms for fingerprint thinning procedure. All of the previously proposed thinning methods try to thin every ridge due to the content of its central pixel and then extracting minutiae based on some other algorithms for denoising and preventing false minutiae detections at islands or spurities. If an algorithm could thin fingerprint ridges except unrecoverable corrupted regions and also could eliminate noise, it will be considered as a good thinning one and no additional processes are needed before minutiae extraction. The proposed method of this paper has such abilities. The proposed algorithm is implemented by applying four boxes of matrices; each of them thins ridges due to a specific direction; i.e., diagonal, horizontal and vertical directions. The proposed algorithm also is able to thin discrete Latin Characters or symbols. For evaluating the proposed method, several robust and reliable experiments have been employed and the results confirm the higher ability of the proposed method in comparison with the other competing one.

In this paper, an innovative blind watermarking algorithm has been proposed for imagery applications. This algorithm has used the coefficients of the discrete wavelet transform of the host image in the form of super trees to embed the predefined binary watermark in the host image. In this scheme, a pseudo random sequence is generated to determine the exact wavelet super trees used for embedding procedure. In the next step, after choosing the maximum and second maximum amplitude coefficients of each super tree, the distance vector between two coefficients is computed. For embedding bit zero of the specified watermark, the values of the distance vector elements are decreased, while for embedding bit 1, those values will be increased based on the proposed formulas. The experimental results show that the proposed algorithm has significant robustness against image processing attacks, especially JPEG compression and also the PSNR value for the watermarked images generated by the proposed method is more than 42 dB. The watermark bits may be inserted into the host image either in spatial or in the transform domain. It is shown that hiding information in the transform domain will lead to a more robust watermarking system against most of the attacks (6). Among these methods, methods based on discrete wavelet transform (DWT) are of great renown. This is due to specific features of space-frequency localization, multi-resolution display, linear computational complexity and the excellent modeling related to Human Vision System