Proposal for DICOM Multiframe Medical Image Integrity and Authenticity

This paper presents a novel algorithm to successfully achieve viable integrity and authenticity addition and verification of n-frame DICOM medical images using cryptographic mechanisms. The aim of this work is the enhancement of DICOM security measures, especially for multiframe images. Current approaches have limitations that should be properly addressed for improved security. The algorithm proposed in this work uses data encryption to provide integrity and authenticity, along with digital signature. Relevant header data and digital signature are used as inputs to cipher the image. Therefore, one can only retrieve the original data if and only if the images and the inputs are correct. The encryption process itself is a cascading scheme, where a frame is ciphered with data related to the previous frames, generating also additional data on image integrity and authenticity. Decryption is similar to encryption, featuring also the standard security verification of the image. The implementation was done in JAVA, and a performance evaluation was carried out comparing the speed of the algorithm with other existing approaches. The evaluation showed a good performance of the algorithm, which is an encouraging result to use it in a real environment.

Topological multi-contour decomposition for image analysis and image retrieval

Successful classification, information retrieval and image analysis tools are intimately related with the quality of the features employed in the process. Pixel intensities, color, texture and shape are, generally, the basis from which most of the features are Computed and used in such fields. This papers presents a novel shape-based feature extraction approach where an image is decomposed into multiple contours, and further characterized by Fourier descriptors. Unlike traditional approaches we make use of topological knowledge to generate well-defined closed contours, which are efficient signatures for image retrieval. The method has been evaluated in the CBIR context and image analysis. The results have shown that the multi-contour decomposition, as opposed to a single shape information, introduced a significant improvement in the discrimination power. (c) 2008 Elsevier B.V. All rights reserved,

Image indexing and retrieval using an ART-2A neural network architecture

Traditional content-based image retrieval (CBIR) systems use low-level features such as colors, shapes, and textures of images. Although, users make queries based on semantics, which are not easily related to such low-level characteristics. Recent works on CBIR confirm that researchers have been trying to map visual low-level characteristics and high-level semantics. The relation between low-level characteristics and image textual information has motivated this article which proposes a model for automatic classification and categorization of words associated to images. This proposal considers a self-organizing neural network architecture, which classifies textual information without previous learning. Experimental results compare the performance results of the text-based approach to an image retrieval system based on low-level features. (c) 2008 Wiley Periodicals, Inc.

Evaluation of follicular lymphoid depletion in the Bursa of Fabricius: an alternative methodology using digital image analysis and artificial neural networks

Fifty Bursa of Fabricius (BF) were examined by conventional optical microscopy and digital images were acquired and processed using Matlab® 6.5 software. The Artificial Neuronal Network (ANN) was generated using Neuroshell® Classifier software and the optical and digital data were compared. The ANN was able to make a comparable classification of digital and optical scores. The use of ANN was able to classify correctly the majority of the follicles, reaching sensibility and specificity of 89% and 96%, respectively. When the follicles were scored and grouped in a binary fashion the sensibility increased to 90% and obtained the maximum value for the specificity of 92%. These results demonstrate that the use of digital image analysis and ANN is a useful tool for the pathological classification of the BF lymphoid depletion. In addition it provides objective results that allow measuring the dimension of the error in the diagnosis and classification therefore making comparison between databases feasible.

IMAGE, MAGIC AND IMAGINATION: CHALLENGES TO THE ANTHROPOLOGICAL TEXT

What different forms of engagement do image and text allow the spectator/reader? We know that text and image communicate, and that all communication depends on a relationship between those who communicate. The objective of this text is therefore to understand the new possibilities available to an anthropology of the expression of knowledge that makes use of images, such as photographs and films.

Patient Characteristics as Predictors of Image Quality and Diagnostic Accuracy of MDCT Compared With Conventional Coronary Angiography for Detecting Coronary Artery Stenoses: CORE-64 Multicenter International Trial

OBJECTIVE. The purpose of the study was to investigate patient characteristics associated with image quality and their impact on the diagnostic accuracy of MDCT for the detection of coronary artery stenosis. MATERIALS AND METHODS. Two hundred ninety-one patients with a coronary artery calcification (CAC) score of <= 600 Agatston units (214 men and 77 women; mean age, 59.3 +/- 10.0 years [SD]) were analyzed. An overall image quality score was derived using an ordinal scale. The accuracy of quantitative MDCT to detect significant (>= 50%) stenoses was assessed using quantitative coronary angiography (QCA) per patient and per vessel using a modified 19-segment model. The effect of CAC, obesity, heart rate, and heart rate variability on image quality and accuracy were evaluated by multiple logistic regression. Image quality and accuracy were further analyzed in subgroups of significant predictor variables. Diagnostic analysis was determined for image quality strata using receiver operating characteristic (ROC) curves. RESULTS. Increasing body mass index (BMI) (odds ratio [OR] = 0.89, p < 0.001), increasing heart rate (OR = 0.90, p < 0.001), and the presence of breathing artifact (OR = 4.97, p = 0.001) were associated with poorer image quality whereas sex, CAC score, and heart rate variability were not. Compared with examinations of white patients, studies of black patients had significantly poorer image quality (OR = 0.58, p = 0.04). At a vessel level, CAC score (10 Agatston units) (OR = 1.03, p = 0.012) and patient age (OR = 1.02, p = 0.04) were significantly associated with the diagnostic accuracy of quantitative MDCT compared with QCA. A trend was observed in differences in the areas under the ROC curves across image quality strata at the vessel level (p = 0.08). CONCLUSION. Image quality is significantly associated with patient ethnicity, BMI, mean scan heart rate, and the presence of breathing artifact but not with CAC score at a patient level. At a vessel level, CAC score and age were associated with reduced diagnostic accuracy.

Supporting content-based image retrieval and computer-aided diagnosis systems with association rule-based techniques

In this work, we take advantage of association rule mining to support two types of medical systems: the Content-based Image Retrieval (CBIR) systems and the Computer-Aided Diagnosis (CAD) systems. For content-based retrieval, association rules are employed to reduce the dimensionality of the feature vectors that represent the images and to improve the precision of the similarity queries. We refer to the association rule-based method to improve CBIR systems proposed here as Feature selection through Association Rules (FAR). To improve CAD systems, we propose the Image Diagnosis Enhancement through Association rules (IDEA) method. Association rules are employed to suggest a second opinion to the radiologist or a preliminary diagnosis of a new image. A second opinion automatically obtained can either accelerate the process of diagnosing or to strengthen a hypothesis, increasing the probability of a prescribed treatment be successful. Two new algorithms are proposed to support the IDEA method: to pre-process low-level features and to propose a preliminary diagnosis based on association rules. We performed several experiments to validate the proposed methods. The results indicate that association rules can be successfully applied to improve CBIR and CAD systems, empowering the arsenal of techniques to support medical image analysis in medical systems. (C) 2009 Elsevier B.V. All rights reserved.

Body image dissatisfaction and eating symptoms in mothers of adolescents with eating disorders

The purpose of the present study was to assess body dissatisfaction and eating symptoms in mothers of eating disorder (ED) female patients and to compare results with those of a control group. The case group consisted of 35 mothers of female adolescents (aged between 10 and 17 yrs) diagnosed with ED who attended the Interdisciplinary Project for Care, Teaching and Research on Eating Disorders in Childhood and Adolescence (PROTAD) at Clinicas Hospital Institute of Psychiatry of the Universidade de Sao Paulo Medical School. Demographic and socioeconomic data were collected. Eating symptoms were assessed using the Eating Attitudes Test (EAT-26) and body image was assessed by the Body Image Questionnaire (BSQ) and Stunkard Figure Rating Scale (FRS). The case group was compared to a control group consisting of 35 mothers of female adolescents (between 10 and 17 years) who attended a private school in the city of Sao Paulo, southeastern Brazil. With regard to EAT, BSQ and FRS scores, we found no statistically significant differences between the two groups. However, we found a positive correlation between BMI and BSQ scores in the control group (but not in the case group) and a positive correlation between EAT and FRS scores in the case group (but not in the control group). It appears to be advantageous to assess body image by combining more than one scale to evaluate additional components of the construct. (Eating Weight Disord. 15: e219-e225, 2010). (C)2010, Editrice Kurtis

Modulation of Bone Morphogenetic Protein-9 Expression and Processing by Insulin, Glucose, and Glucocorticoids: Possible Candidate for Hepatic Insulin-Sensitizing Substance

Bone morphogenetic protein 9 (BMP-9), a member of the TGF-beta superfamily predominantly expressed in nonparenchymal liver cells, has been demonstrated to improve glucose homeostasis in diabetic mice. Along with this therapeutic effect, BMP-9 was proposed as a candidate for the hepatic insulin-sensitizing substance ( HISS). Whether BMP-9 plays a physiological role in glucose homeostasis is still unknown. In the present study, we show that BMP-9 expression and processing is severely reduced in the liver of insulin-resistant rats. BMP-9 expression and processing was directly stimulated by in situ exposition of the liver to the combination of glucose and insulin and oral glucose in overnight fasted rats. Additionally, prolonged fasting ( 72 h) abrogated refeeding-induced BMP-9 expression and processing. Previous exposition to dexamethasone, a known inductor of insulin resistance, reduced BMP-9 processing stimulated by the combination of insulin and glucose. Finally, we show that neutralization of BMP-9 with an anti-BMP-9 antibody induces glucose intolerance and insulin resistance in 12-h fasted rats. Collectively, the present results demonstrate that BMP-9 plays an important role in the control of glucose homeostasis of the normal rat. Additionally, BMP-9 is expressed and processed in an HISS-like fashion, which is impaired in the presence of insulin resistance. BMP-9 regulation according to the feeding status and the presence of diabetogenic factors reinforces the hypothesis that BMP-9 might exert the role of HISS in glucose homeostasis physiology. ( Endocrinology 149: 6326-6335, 2008)

Improvements on ICA mixture models for image pre-processing and segmentation

Today several different unsupervised classification algorithms are commonly used to cluster similar patterns in a data set based only on its statistical properties. Specially in image data applications, self-organizing methods for unsupervised classification have been successfully applied for clustering pixels or group of pixels in order to perform segmentation tasks. The first important contribution of this paper refers to the development of a self-organizing method for data classification, named Enhanced Independent Component Analysis Mixture Model (EICAMM), which was built by proposing some modifications in the Independent Component Analysis Mixture Model (ICAMM). Such improvements were proposed by considering some of the model limitations as well as by analyzing how it should be improved in order to become more efficient. Moreover, a pre-processing methodology was also proposed, which is based on combining the Sparse Code Shrinkage (SCS) for image denoising and the Sobel edge detector. In the experiments of this work, the EICAMM and other self-organizing models were applied for segmenting images in their original and pre-processed versions. A comparative analysis showed satisfactory and competitive image segmentation results obtained by the proposals presented herein. (C) 2008 Published by Elsevier B.V.

Effects of sex hormonal levels and phases of the menstrual cycle in the processing of emotional faces

Several neuropsychiatry disorders have shown a sexual dimorphism in their incidence, symptom profile and therapeutic response. A better understanding of the impact of sex hormones in emotional processing sexual dimorphism could bring tight to this important clinical finding. Some studies have provided evidence of sex differences in the identification of emotional faces, however, results are inconsistent and such inconsistency could be related to the lack of experimental control of the sex hormone status of participants. More recently, a few studies evaluated the modulation of facial emotion recognition by the phase of the menstrual cycle and sex hormones, however, none of them directly compared these results with a group of men. We evaluated the accuracy of facial emotion recognition in 40 healthy volunteers. Eleven women were assigned to early follicular group, nine women to the ovulatory group and 10 women to luteal group, depending on the phase of menstrual cycle, and a group of 10 men were also evaluated. Estrogen, progesterone and testosterone levels were assessed. The performance of the groups in the identification of emotional faces varied depending on the emotion. Early follicular group were more accurate to perceive angry faces than all other groups. Sadness was more accurately recognized by early follicular group than by luteal group and regarding the recognition of fearful faces a trend to a better performance and a significantly higher accuracy was observed, respectively, in the early follicular group and in the ovulatory group, in comparison to men. In women, estrogen negatively correlated to the accuracy in perception of angry mate faces. Our results indicate sex hormones to be implicated in a sexual dimorphism in facial emotion recognition, and highlight the importance of estrogen specifically in the recognition of negative emotions such as sadness, anger and fear. (C) 2009 Elsevier Ltd. All rights reserved.

Thermoplastic starch modified during melt processing with organic acids: The effect of molar mass on thermal and mechanical properties

Thermoplastic starch (TPS) was modified with ascorbic acid and citric acid by melt processing of native starch with glycerol as plasticizer in an intensive batch mixer at 160 degrees C. It was found that the molar mass decreases with acid content and processing time causing the reduction in melting temperature (T(m)). As observed by the results of X-ray diffraction and DSC measurements, crystallinity was not changed by the reaction with organic acids. T(m) depression with falling molar mass was interpreted on the basis of the effect of concentration of end-chain units, which act as diluents. FTIR did not show any appreciable change in starch chemical compositions, leading to the conclusion that the main changes observed were produced by the variation in molar mass of the material. We demonstrated that it is possible to decrease melt viscosity without the need for more plasticizer thus avoiding side-effects such as an increase in water affinity or relevant changes in the dynamic mechanical properties. (C) 2010 Elsevier B.V. All rights reserved.

HYBRID AND INCREMENTAL FUZZY LEARNING FOR HUMAN SKIN DETECTION

In this paper, a framework for detection of human skin in digital images is proposed. This framework is composed of a training phase and a detection phase. A skin class model is learned during the training phase by processing several training images in a hybrid and incremental fuzzy learning scheme. This scheme combines unsupervised-and supervised-learning: unsupervised, by fuzzy clustering, to obtain clusters of color groups from training images; and supervised to select groups that represent skin color. At the end of the training phase, aggregation operators are used to provide combinations of selected groups into a skin model. In the detection phase, the learned skin model is used to detect human skin in an efficient way. Experimental results show robust and accurate human skin detection performed by the proposed framework.

SKAN: Skin Scanner - System for Skin Cancer Detection Using Adaptive Techniques

SKAN: Skin Scanner - System for Skin Cancer Detection Using Adaptive Techniques - combines computer engineering concepts with areas like dermatology and oncology. Its objective is to discern images of skin cancer, specifically melanoma, from others that show only common spots or other types of skin diseases, using image recognition. This work makes use of the ABCDE visual rule, which is often used by dermatologists for melanoma identification, to define which characteristics are analyzed by the software. It then applies various algorithms and techniques, including an ellipse-fitting algorithm, to extract and measure these characteristics and decide whether the spot is a melanoma or not. The achieved results are presented with special focus on the adaptive decision-making and its effect on the diagnosis. Finally, other applications of the software and its algorithms are presented.

Recognition of cyclic, acyclic, exocyclic, and spiro Acetals via structurally diagnostic ion/molecule reactions with the (CH3)(2)N-C+=O acylium ion

Reactions of the model acylium ion (CH3)(2)N-C+=O with acyclic, exocyclic, and Spiro acetals of the general formula (RO)-O-1-(CRR4)-R-3-OR2-upole mass spectrometry. Characteristic intrinsic reactivities were observed for each of these classes of acetals. The two most Characteristic intrinsic reactivities were observed for each of these classes of acetals. The two most common reactions observed were hydride and alkoxy anion [(RO-)-O-1 and (RO-)-O-2] abstraction. Other specific reactions were also observed: (a) a secondary polar [4(+) + 2] cycloaddition for acetals bearing alpha,beta-unsaturated R-3 or R-4 substituents and (b) OH- abstraction for exocyclic and spiro acetals. These structurally diagnostic reactions, in conjunction with others observed previously for cyclic acetals, are shown to reveal the class of the acetal molecule and its ring type and substituents and to permit their recognition and distinction from other classes of isomeric molecules.