Examining the Psychometric Properties of the Revised Scale of Economic Abuse among Iranian Women.

Economic abuse, in the context of intimate relationships, is a pervasive form of violence that merits further empirical attention. We know from limited research that the rates of economic abuse appear to be high in Iran; however, there is a lack of culturally appropriate measures that can assess the extent to which women experience economic harm as a result of their partners' actions. The present study was conducted with the aims of (a) investigating the psychometric properties of the 14-item Revised Scale of Economic Abuse (SEA2) which was translated into Persian for this study and (b) examining the prevalence of economic abuse among a sample of 371 married housewives in Qazvin, Iran. Confirmatory factor analysis supports the two-factor structure of the SEA2, with the exception of one item. Composite reliability and Cronbach's alpha demonstrated good internal consistency. The average variance extracted method, along with correlations with other financial variables, demonstrated evidence of good convergent validity. Correlations with related, but distinct forms of abuse, support the scale's discriminant validity. Based on the collective findings, this measure can be used as a reliable and valid tool to study economic abuse among Iranian women which, within our sample, appears to be a common phenomenon. Implications for future research and practice are discussed.

An investigation of self-injury in female adolescents: a qualitative study.

This study aimed to investigate the phenomenon of self-injury among female adolescents. The research was qualitative, and the sampling method was purposive non-random; the sample size was 20 Iranian girl adolescents aged 13-15 years who had experienced non-suicidal self-injury. Data were collected through semi-structured interviews. The data analysis process was performed during three coding steps (open, axial, selective), through which the basic codes and categories were identified. Study results indicate that the main factors in adolescents' self-injury were individual or psychological (thoughts, emotions, and behaviors) and social (peers, family, communication with different gender, communication with others, media/cyberspace, school, and economic). In the former, the role of emotions was remarkable, while in the latter, the family played a key role. Further, results revealed that communication within the family was an important motivating and sustentative factor in adolescents' self-injury. The results can help counselors in working with adolescents who self-injure; results can also be used in the development and implementation of treatment plans.

Simplification of neural networks for skin lesion image segmentation using color channel pruning.

Automatic analysis of skin abnormality is an effective way for medical experts to facilitate diagnosis procedures and improve their capabilities. Efficient and accurate methods for analysis of the skin abnormalities such as convolutional neural networks (CNNs) are typically complex. Hence, the implementation of such complex structures in portable medical instruments is not feasible due to power and resource limitations. CNNs can extract features from the skin abnormality images automatically. To reduce the burden of the network for feature extraction, which can lead to the network simplicity, proper input color channels could be selected. In this paper, a pruning framework is proposed to simplify these complex structures through the selection of most informative color channels and simplification of the network. Moreover, hardware requirements of different network structures are identified to analyze the complexity of different networks. Experimental results are conducted for segmentation of images from two publicly available datasets of both dermoscopy and non-dermoscopy images. Simulation results show that using the proposed color channel selection method, simple and efficient neural network structures can be applied for segmentation of skin abnormalities.

Vessel extraction in X-ray angiograms using deep learning.

Coronary artery disease (CAD) is the most common type of heart disease which is the leading cause of death all over the world. X-ray angiography is currently the gold standard imaging technique for CAD diagnosis. These images usually suffer from low quality and presence of noise. Therefore, vessel enhancement and vessel segmentation play important roles in CAD diagnosis. In this paper a deep learning approach using convolutional neural networks (CNN) is proposed for detecting vessel regions in angiography images. Initially, an input angiogram is preprocessed to enhance its contrast. Afterward, the image is evaluated using patches of pixels and the network determines the vessel and background regions. A set of 1,040,000 patches is used in order to train the deep CNN. Experimental results on angiography images of a dataset show that our proposed method has a superior performance in extraction of vessel regions.

Robust watermarking in non-ROI of medical images based on DCT-DWT.

Increasing demand and utilization of telemedicine require transmission of medical information and images over internet. Since authenticity of received images is crucial and patient's information should be included with minimum changes in images, robust watermarking schemes are needed. In this paper, we propose a robust watermark method that embeds patient's information outside the region of interest (ROI) in medical image. In order to find appropriate regions for embedding, we use saliency as a means of measuring importance of regions and find blocks having minimum overlap with the ROI. The algorithm employs wavelet transform and also discrete cosine transform (DCT) domains in the embedding stage and redundantly embeds watermark to increase robustness against possible alterations. Moreover, voting is utilized in the extraction phase. Experimental results show the efficiency of the proposed method and better results are obtained as compared to comparable methods with same size of the watermarked data.

Automatic detection of melanoma using broad extraction of features from digital images.

Automatic and reliable diagnosis of skin cancer, as a smartphone application, is of great interest. Among different types of skin cancers, melanoma is the most dangerous one which causes most deaths. Meanwhile, melanoma is curable if it were diagnosed in its early stages. In this paper we propose an efficient system for prescreening of pigmented skin lesions for malignancy using general-purpose digital cameras. These images can be captured by a smartphone or a digital camera. This could be beneficial in different applications, such as computer aided diagnosis and telemedicine applications. It could assist dermatologists, or smartphone users, evaluate risk of suspicious moles. The proposed method enhances borders and extracts a broad set of dermatologically important features. These discriminative features allow classification of lesions into two groups of melanoma and benign. This method is computationally appropriate as a smartphone application. Experimental results show that our proposed method is superior in diagnosis accuracy compared to state-of-the-art methods.

Melanoma detection by analysis of clinical images using convolutional neural network.

Melanoma, most threatening type of skin cancer, is on the rise. In this paper an implementation of a deep-learning system on a computer server, equipped with graphic processing unit (GPU), is proposed for detection of melanoma lesions. Clinical (non-dermoscopic) images are used in the proposed system, which could assist a dermatologist in early diagnosis of this type of skin cancer. In the proposed system, input clinical images, which could contain illumination and noise effects, are preprocessed in order to reduce such artifacts. Afterward, the enhanced images are fed to a pre-trained convolutional neural network (CNN) which is a member of deep learning models. The CNN classifier, which is trained by large number of training samples, distinguishes between melanoma and benign cases. Experimental results show that the proposed method is superior in terms of diagnostic accuracy in comparison with the state-of-the-art methods.

Hardware image assessment for wireless endoscopy capsules.

Wireless capsule endoscopy is a new technology in the realm of telemedicine that has many advantages over the traditional endoscopy systems. Transmitted images should help diagnosis of diseases of the gastrointestinal tract. Two important technical challenges for the manufacturers of these capsules are power consumption and size of the circuitry. Also, the system must be fast enough for real-time processing of image or video data. To solve this problem, many hardware designs have been proposed for implementation of the image processing unit. In this paper we propose an architecture that could be used for the assessment of endoscopy images. The assessment allows avoidance of transmission of medically useless images. Hence, volume of data is reduced for more efficient transmission of images by the endoscopy capsule. This is done by color space conversion and moment calculation of images captured by the capsule. The inputs of the proposed architecture are RGB image frames and the outputs are images with converted colors and calculated image moments. Experimental results indicate that the proposed architecture has low complexity and is appropriate for a real-time application.

Automatic segmentation of multimodal brain tumor images based on classification of super-voxels.

Despite the rapid growth in brain tumor segmentation approaches, there are still many challenges in this field. Automatic segmentation of brain images has a critical role in decreasing the burden of manual labeling and increasing robustness of brain tumor diagnosis. We consider segmentation of glioma tumors, which have a wide variation in size, shape and appearance properties. In this paper images are enhanced and normalized to same scale in a preprocessing step. The enhanced images are then segmented based on their intensities using 3D super-voxels. Usually in images a tumor region can be regarded as a salient object. Inspired by this observation, we propose a new feature which uses a saliency detection algorithm. An edge-aware filtering technique is employed to align edges of the original image to the saliency map which enhances the boundaries of the tumor. Then, for classification of tumors in brain images, a set of robust texture features are extracted from super-voxels. Experimental results indicate that our proposed method outperforms a comparable state-of-the-art algorithm in term of dice score.

Robust catheter identification and tracking in X-ray angiographic sequences.

Coronary artery disease (CAD) is one of the major causes of death worldwide. Today X-ray angiography is a standard method for CAD diagnosis. Usually, the quality of these images is not good enough. Noise, camera and heart motions, non-uniform illumination and even the presence of catheter are sources of quality degradation. The existence of catheter can produce difficulties in vessel extraction methods because catheter is structurally similar to arteries. In this paper we propose a fully automatic method for catheter detection and tracking during the whole angiography sequence. In this method with a vesselness map, we smooth each frame using guided filter. The catheter is detected in the first frame using Hough transform. We then fit a second order polynomial on the catheter and accurately track it throughout the sequence. Our method is tested on 25 X-ray angiography sequences where a precision of 0.9597 is achieved.

Region of interest extraction for lossless compression of bone X-ray images.

For few decades digital X-ray imaging has been one of the most important tools for medical diagnosis. With the advent of distance medicine and the use of big data in this respect, the need for efficient storage and online transmission of these images is becoming an essential feature. Limited storage space and limited transmission bandwidth are the main challenges. Efficient image compression methods are lossy while the information of medical images should be preserved with no change. Hence, lossless compression methods are necessary for this purpose. In this paper, a novel method has been proposed to eliminate the non-ROI data from bone X-ray images. Background pixels do not contain any valuable medical information. The proposed method is based on the histogram dispersion method. ROI is separated from the background and it is compressed with a lossless compression method to preserve medical information of the image. Compression ratios of the implemented results show that the proposed algorithm is capable of effective reduction of the statistical and spatial redundancies.

Adaptive stereo medical image watermarking using non-corresponding blocks.

Today with the advent of technology in different medical imaging fields, the use of stereoscopic images has increased. Furthermore, with the rapid growth in telemedicine for remote diagnosis, treatment, and surgery, there is a need for watermarking. This is for copyright protection and tracking of digital media. Also, the efficient use of bandwidth for transmission of such data is another concern. In this paper an adaptive watermarking scheme is proposed that considers human visual system in depth perception. Our proposed scheme modifies maximum singular values of wavelet coefficients of stereo pair for embedding watermark bits. Experimental results show high 3D visual quality of watermarked video frames. Moreover, comparison with a compatible state of the art method shows that the proposed method is highly robust against attacks such as AWGN, salt and pepper noise, and JPEG compression.

Witches'-Broom Disease of Lime Affects Seed Germination and Seedling Growth But Is Not Seed Transmissible.

Witches'-broom disease of lime (WBDL), caused by 'Candidatus Phytoplasma aurantifolia', has devastated many Mexican lime orchards and is currently a threat to lime production in neighboring provinces of southern Iran. Several reports have suggested transmission and spread of WBDL phytoplasma through the seed of infected plants. In the present study, claims of seed transmission of this phytoplasma were examined. Fruit were collected from infected trees in the infested areas of Minab (Hormozgan Province) and from symptomless trees in noninfested areas. Lime seed from symptomless and witches'-broom-affected trees were sown in separate beds in an insect-proof screenhouse and the resulting seedlings were examined for phytoplasmal infection. Leaf, stem, and root samples were collected from both groups of seedlings every 3 months for 2 years and tested for WBDL phytoplasma using direct and nested polymerase chain reaction (PCR). Repeated PCR tests on the seedlings did not reveal the presence of phytoplasmal DNA. Likewise, symptoms of the disease were not observed on these seedlings after 2 years. PCR assays detected the phytoplasma in coats of some seed from infected trees; however, no excised embryos were positive for the phytoplasma. All positive PCR results were confirmed by restriction fragment length polymorphism assay. One-year-old seedlings derived from seed of noninfected plants appeared more vigorous in terms of height, number of leaves, and fresh weight of shoot compared with those from infected trees. The germination percentage, mean daily germination, peak value, and germination value were significantly higher for seed of fruit from noninfected trees and seed from fruit on asymptomatic branches of infected trees than those from fruit on symptomatic branches of infected trees.