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.

Transvaginal ultrasonography with bowel preparation is able to predict the number of lesions and rectosigmoid layers affected in cases of deep endometriosis, defining surgical strategy

Successful surgical treatment of deep bowel endometriosis depends on obtaining detailed information about the lesions, prior to the procedure. The objective of this study was to determine the capability of transvaginal ultrasonography with bowel preparation (TVUS-BP) to predict the presence of one or more rectosigmoid nodules and the deepest bowel layer affected by the disease. A prospective study of 194 patients with clinical and TVUS-BP suspected deep endometriosis submitted to videolaparoscopy. Image data were compared with surgical and histological results. With respect to bowel nodule detection and presence of at least two rectosigmoid lesions, TVUS-BP had a sensitivity of 97 and 81%, specificity 100 and 99%, positive predictive value (PPV) 100 and 93% and negative predictive value (NPV) 98 and 96%, respectively. Regarding diagnosis of infiltration of the submucosal/mucosal layer, TVUS-BP had a sensitivity of 83%, specificity 94%, PPV 77%, NPV 96%. These findings show that TVUS-BP is an adequate exam for evaluating the presence of one or more rectosigmoid nodules and the deepest layer affected in deep infiltrating bowel endometriosis, confirming the importance of this technique for defining the most appropriate surgical strategy to be implemented.

Generating segmented meshes from textured color images

This paper presents a new framework for generating triangular meshes from textured color images. The proposed framework combines a texture classification technique, called W-operator, with Imesh, a method originally conceived to generate simplicial meshes from gray scale images. An extension of W-operators to handle textured color images is proposed, which employs a combination of RGB and HSV channels and Sequential Floating Forward Search guided by mean conditional entropy criterion to extract features from the training data. The W-operator is built into the local error estimation used by Imesh to choose the mesh vertices. Furthermore, the W-operator also enables to assign a label to the triangles during the mesh construction, thus allowing to obtain a segmented mesh at the end of the process. The presented results show that the combination of W-operators with Imesh gives rise to a texture classification-based triangle mesh generation framework that outperforms pixel based methods. Crown Copyright (C) 2009 Published by Elsevier Inc. All rights reserved.

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.

Chrysotile effects on human lung cell carcinoma in culture: 3-D reconstruction and DNA quantification by image analysis

Background: Chrysotile is considered less harmful to human health than other types of asbestos fibers. Its clearance from the lung is faster and, in comparison to amphibole forms of asbestos, chrysotile asbestos fail to accumulate in the lung tissue due to a mechanism involving fibers fragmentation in short pieces. Short exposure to chrysotile has not been associated with any histopathological alteration of lung tissue. Methods: The present work focuses on the association of small chrysotile fibers with interphasic and mitotic human lung cancer cells in culture, using for analyses confocal laser scanning microscopy and 3D reconstructions. The main goal was to perform the analysis of abnormalities in mitosis of fibers-containing cells as well as to quantify nuclear DNA content of treated cells during their recovery in fiber-free culture medium. Results: HK2 cells treated with chrysotile for 48 h and recovered in additional periods of 24, 48 and 72 h in normal medium showed increased frequency of multinucleated and apoptotic cells. DNA ploidy of the cells submitted to the same chrysotile treatment schedules showed enhanced aneuploidy values. The results were consistent with the high frequency of multipolar spindles observed and with the presence of fibers in the intercellular bridge during cytokinesis. Conclusion: The present data show that 48 h chrysotile exposure can cause centrosome amplification, apoptosis and aneuploid cell formation even when long periods of recovery were provided. Internalized fibers seem to interact with the chromatin during mitosis, and they could also interfere in cytokinesis, leading to cytokinesis failure which forms aneuploid or multinucleated cells with centrosome amplification.

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.

Image reconstruction affects computer tomographic assessment of lung hyperinflation

Objectives: Lung hyperinflation may be assessed by computed tomography (CT). As shown for patients with emphysema, however, CT image reconstruction affects quantification of hyperinflation. We studied the impact of reconstruction parameters on hyperinflation measurements in mechanically ventilated (MV) patients. Design: Observational analysis. Setting: A University hospital-affiliated research Unit. Patients: The patients were MV patients with injured (n = 5) or normal lungs (n = 6), and spontaneously breathing patients (n = 5). Interventions: None. Measurements and results: Eight image series involving 3, 5, 7, and 10 mm slices and standard and sharp filters were reconstructed from identical CT raw data. Hyperinflated (V-hyper), normally (V-normal), poorly (V-poor), and nonaerated (V-non) volumes were calculated by densitometry as percentage of total lung volume (V-total). V-hyper obtained with the sharp filter systematically exceeded that with the standard filter showing a median (interquartile range) increment of 138 (62-272) ml corresponding to approximately 4% of V-total. In contrast, sharp filtering minimally affected the other subvolumes (V-normal, V-poor, V-non, and V-total). Decreasing slice thickness also increased V-hyper significantly. When changing from 10 to 3 mm thickness, V-hyper increased by a median value of 107 (49-252) ml in parallel with a small and inconsistent increment in V-non of 12 (7-16) ml. Conclusions: Reconstruction parameters significantly affect quantitative CT assessment of V-hyper in MV patients. Our observations suggest that sharp filters are inappropriate for this purpose. Thin slices combined with standard filters and more appropriate thresholds (e.g., -950 HU in normal lungs) might improve the detection of V-hyper. Different studies on V-hyper can only be compared if identical reconstruction parameters were used.

Texture Image Analysis in Differentiating Malignant from Benign Adrenal Cortical Tumors in Children and Adults

Objective: To investigate the possible role of chromatin texture parameters, nuclear morphology, DNA ploidy and clinical functional status in discriminating benign from malignant adrenocortical tumors (ACT). Patients and Methods: Forty-eight cases of clinically benign (n=40) and clinically malignant (n=8) ACT with a minimum of 5-years` follow-up were evaluated for chromatin texture parameters (run length, standard deviation, configurable run length, valley, slope, peak and other 21 Markovian features that describe the distribution of the chromatin in the nucleus), nuclear morphology (nuclear area, nuclear perimeter, nuclear maximum and minumum diameter, nuclear shape), and DNA ploidy. Nuclear parameters were evaluated in Feulgen-stained 5 mu m paraffin-sections analyzed using a CAS 200 image analyzer. Results: Since ACTs present different biological features in children and adults, patients were divided into two groups: children (<= 15 years) and adults (>15 years). In the group of children DNA ploidy presented a marginal significance (p=0.05) in discriminating ACTs. None of the parameters discriminated between malignant and benign ACT in the adult group. Conclusion: ACTs are uncommon and definitive predictive criteria for malignancy remain uncertain, particularly in children. Our data point to DNA content evaluated by image analysis as a new candidate tool for this challenging task. Texture image analysis did not help to differentiate malignant from benign adrenal cortical tumors in children and adults.

IMAGE QUANTIFICATION FOR RADIATION DOSE CALCULATIONS-LIMITATIONS AND UNCERTAINTIES

Radiation dose calculations in nuclear medicine depend on quantification of activity via planar and/or tomographic imaging methods. However, both methods have inherent limitations, and the accuracy of activity estimates varies with object size, background levels, and other variables. The goal of this study was to evaluate the limitations of quantitative imaging with planar and single photon emission computed tomography (SPECT) approaches, with a focus on activity quantification for use in calculating absorbed dose estimates for normal organs and tumors. To do this we studied a series of phantoms of varying complexity of geometry, with three radionuclides whose decay schemes varied from simple to complex. Four aqueous concentrations of (99m)Tc, (131)I, and (111)In (74, 185, 370, and 740 kBq mL(-1)) were placed in spheres of four different sizes in a water-filled phantom, with three different levels of activity in the surrounding water. Planar and SPECT images of the phantoms were obtained on a modern SPECT/computed tomography (CT) system. These radionuclides and concentration/background studies were repeated using a cardiac phantom and a modified torso phantom with liver and ""tumor"" regions containing the radionuclide concentrations and with the same varying background levels. Planar quantification was performed using the geometric mean approach, with attenuation correction (AC), and with and without scatter corrections (SC and NSC). SPECT images were reconstructed using attenuation maps (AM) for AC; scatter windows were used to perform SC during image reconstruction. For spherical sources with corrected data, good accuracy was observed (generally within +/- 10% of known values) for the largest sphere (11.5 mL) and for both planar and SPECT methods with (99m)Tc and (131)I, but were poorest and deviated from known values for smaller objects, most notably for (111)In. SPECT quantification was affected by the partial volume effect in smaller objects and generally showed larger errors than the planar results in these cases for all radionuclides. For the cardiac phantom, results were the most accurate of all of the experiments for all radionuclides. Background subtraction was an important factor influencing these results. The contribution of scattered photons was important in quantification with (131)I; if scatter was not accounted for, activity tended to be overestimated using planar quantification methods. For the torso phantom experiments, results show a clear underestimation of activity when compared to previous experiment with spherical sources for all radionuclides. Despite some variations that were observed as the level of background increased, the SPECT results were more consistent across different activity concentrations. Planar or SPECT quantification on state-of-the-art gamma cameras with appropriate quantitative processing can provide accuracies of better than 10% for large objects and modest target-to-background concentrations; however when smaller objects are used, in the presence of higher background, and for nuclides with more complex decay schemes, SPECT quantification methods generally produce better results. Health Phys. 99(5):688-701; 2010

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.

An association rule-based method to support medical image diagnosis with efficiency

In this paper, we propose a method based on association rule-mining to enhance the diagnosis of medical images (mammograms). It combines low-level features automatically extracted from images and high-level knowledge from specialists to search for patterns. Our method analyzes medical images and automatically generates suggestions of diagnoses employing mining of association rules. The suggestions of diagnosis are used to accelerate the image analysis performed by specialists as well as to provide them an alternative to work on. The proposed method uses two new algorithms, PreSAGe and HiCARe. The PreSAGe algorithm combines, in a single step, feature selection and discretization, and reduces the mining complexity. Experiments performed on PreSAGe show that this algorithm is highly suitable to perform feature selection and discretization in medical images. HiCARe is a new associative classifier. The HiCARe algorithm has an important property that makes it unique: it assigns multiple keywords per image to suggest a diagnosis with high values of accuracy. Our method was applied to real datasets, and the results show high sensitivity (up to 95%) and accuracy (up to 92%), allowing us to claim that the use of association rules is a powerful means to assist in the diagnosing task.

HIV/AIDS: use of the ICF in Brazil and South Africa - comparative data from four cross-sectional studies

Introduction Human immunodeficiency virus (HIV) is a serious disease which can be associated with various activity limitations and participation restrictions. The aim of this paper was to describe how HIV affects the functioning and health of people within different environmental contexts, particularly with regard to access to medication. Method Four cross-sectional studies, three in South Africa and one in Brazil, had applied the International Classification of Functioning, Disability and Health (ICF) as a classification instrument to participants living with HIV. Each group was at a different stage of the disease. Only two groups had had continuing access to antiretroviral therapy. The existence of these descriptive sets enabled comparison of the disability experienced by people living with HIV at different stages of the disease and with differing access to antiretroviral therapy. Results Common problems experienced in all groups related to weight maintenance, with two-thirds of the sample reporting problems in this area. Mental functions presented the most problems in all groups, with sleep (50%, 92/185), energy and drive (45%, 83/185), and emotional functions (49%, 90/185) being the most affected. In those on long-term therapy, body image affected 93% (39/42) and was a major problem. The other groups reported pain as a problem, and those with limited access to treatment also reported mobility problems. Cardiopulmonary functions were affected in all groups. Conclusion Functional problems occurred in the areas of impairment and activity limitation in people at advanced stages of HIV, and more limitations occurred in the area of participation for those on antiretroviral treatment. The ICF provided a useful framework within which to describe the functioning of those with HIV and the impact of the environment. Given the wide spectrum of problems found, consideration could be given to a number of ICF core sets that are relevant to the different stages of HIV disease. (C) 2010 Chartered Society of Physiotherapy. Published by Elsevier Ltd. 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

PCA Tomography: how to extract information from data cubes

Astronomy has evolved almost exclusively by the use of spectroscopic and imaging techniques, operated separately. With the development of modern technologies, it is possible to obtain data cubes in which one combines both techniques simultaneously, producing images with spectral resolution. To extract information from them can be quite complex, and hence the development of new methods of data analysis is desirable. We present a method of analysis of data cube (data from single field observations, containing two spatial and one spectral dimension) that uses Principal Component Analysis (PCA) to express the data in the form of reduced dimensionality, facilitating efficient information extraction from very large data sets. PCA transforms the system of correlated coordinates into a system of uncorrelated coordinates ordered by principal components of decreasing variance. The new coordinates are referred to as eigenvectors, and the projections of the data on to these coordinates produce images we will call tomograms. The association of the tomograms (images) to eigenvectors (spectra) is important for the interpretation of both. The eigenvectors are mutually orthogonal, and this information is fundamental for their handling and interpretation. When the data cube shows objects that present uncorrelated physical phenomena, the eigenvector`s orthogonality may be instrumental in separating and identifying them. By handling eigenvectors and tomograms, one can enhance features, extract noise, compress data, extract spectra, etc. We applied the method, for illustration purpose only, to the central region of the low ionization nuclear emission region (LINER) galaxy NGC 4736, and demonstrate that it has a type 1 active nucleus, not known before. Furthermore, we show that it is displaced from the centre of its stellar bulge.

Visual analysis of image collections

Multidimensional Visualization techniques are invaluable tools for analysis of structured and unstructured data with variable dimensionality. This paper introduces PEx-Image-Projection Explorer for Images-a tool aimed at supporting analysis of image collections. The tool supports a methodology that employs interactive visualizations to aid user-driven feature detection and classification tasks, thus offering improved analysis and exploration capabilities. The visual mappings employ similarity-based multidimensional projections and point placement to layout the data on a plane for visual exploration. In addition to its application to image databases, we also illustrate how the proposed approach can be successfully employed in simultaneous analysis of different data types, such as text and images, offering a common visual representation for data expressed in different modalities.