86 research outputs found
Hierarchical Cluster Analysis to Aid Diagnostic Image Data Visualization of MS and Other Medical Imaging Modalities
Perceiving abnormal regions in the images of different medical modalities plays a crucial role in diagnosis
and subsequent treatment planning. In medical images to visually perceive abnormalities’ extent and
boundaries requires substantial experience. Consequently, manually drawn region of interest (ROI) to
outline boundaries of abnormalities suffers from limitations of human perception leading to inter-observer
variability. As an alternative to human drawn ROI, it is proposed the use of a computer-based segmenta-
tion algorithm to segment digital medical image data.
Hierarchical Clustering-based Segmentation (HCS) process is a generic unsupervised segmentation
process that can be used to segment dissimilar regions in digital images. HCS process generates a hierarchy
of segmented images by partitioning an image into its constituent regions at hierarchical levels of allowable
dissimilarity between its different regions. The hierarchy represents the continuous merging of similar,
spatially adjacent, and/or disjoint regions as the allowable threshold value of dissimilarity between regions,
for merging, is gradually increased.
This chapter discusses in detail first the implementation of the HCS process, second the implementa-
tion details of how the HCS process is used for the presentation of multi-modal imaging data (MALDI and
MRI) of a biological sample, third the implementation details of how the process is used as a perception
aid for X-ray mammogram readers, and finally the implementation details of how it is used as an interpreta-
tion aid for the interpretation of Multi-parametric Magnetic Resonance Imaging (mpMRI) of the Prostate
Influence of permanent night work on the circadian rhythm of blood pressure
Abstract. Night workers exercise their labours activities and rest in contrary schedules to the chronobiological standards. This inversion leads the body to several adaptations, including changes in the circadian rhythm of blood pressure (BP). Objectives: To evaluate the BP in individuals who perform work at night, in order to objectively detail the BP circadian rhythm adaptations infixed night workers. Methods: A cross-sectional study enrolling 23 fixed night workers, both genders, was performed, with 24h BP measured with ambulatory blood pressure monitoring (ABPM) during a normal working day. Risk factors, anthropometric and lifestyle information were collected using a standard questionnaire. Results: Ambulatory BP demonstrated a pattern of adaptation to the sleep/activity cycle in all participants. BP dropped during the sleeping period (mean drop: -11.35±6.85) and was higher during the awakening period, reaching the highest results and greater BP variability during the working period. The chronobiological adaptation of the 24h BP was not dependent on sociodemographic or clinical characteristics. In addition, age, male gender, obesity, and those working less time were associated with higher BP mean values. Conclu-sions: The circadian rhythm of BP follows the working circadian profile of the individual.info:eu-repo/semantics/publishedVersio
Segmentation of Fluorescence Microscopy Cell Images Using Unsupervised Mining
The accurate measurement of cell and nuclei contours are critical for the sensitive and specific detection of changes in normal cells in several medical informatics disciplines. Within microscopy, this task is facilitated using fluorescence cell stains, and segmentation is often the first step in such approaches. Due to the complex nature of cell issues and problems inherent to microscopy, unsupervised mining approaches of clustering can be incorporated in the segmentation of cells. In this study, we have developed and evaluated the performance of multiple unsupervised data mining techniques in cell image segmentation. We adapt four distinctive, yet complementary, methods for unsupervised learning, including those based on k-means clustering, EM, Otsu’s threshold, and GMAC. Validation measures are defined, and the performance of the techniques is evaluated both quantitatively and qualitatively using synthetic and recently published real data. Experimental results demonstrate that k-means, Otsu’s threshold, and GMAC perform similarly, and have more precise segmentation results than EM. We report that EM has higher recall values and lower precision results from under-segmentation due to its Gaussian model assumption. We also demonstrate that these methods need spatial information to segment complex real cell images with a high degree of efficacy, as expected in many medical informatics applications
A Geologically Based Indoor-Radon Potential Map of Kentucky
We combined 71,930 short-term (median duration 4 days) home radon test results with 1:24,000-scale bedrock geologic map coverage of Kentucky to produce a statewide geologically based indoor-radon potential map. The test results were positively skewed with a mean of 266 Bq/m3, median of 122 Bq/m3, and 75th percentile of 289 Bq/m3. We identified 106 formations with ≥10 test results. Analysis of results from 20 predominantly monolithologic formations showed indoor-radon concentrations to be positively skewed on a formation-by-formation basis, with a proportional relationship between sample means and standard deviations. Limestone (median 170 Bq/m3) and dolostone (median 130 Bq/m3) tended to have higher indoor-radon concentrations than siltstones and sandstones (median 67 Bq/m3) or unlithified surficial deposits (median 63 Bq/m3). Individual shales had median values ranging from 67 to 189 Bq/m3; the median value for all shale values was 85 Bq/m3. Percentages of values falling above the U.S. Environmental Protection Agency (EPA) action level of 148 Bq/m3 were sandstone and siltstone: 24%, unlithified clastic: 21%, dolostone: 46%, limestone: 55%, and shale: 34%. Mississippian limestones, Ordovician limestones, and Devonian black shales had the highest indoor-radon potential values in Kentucky. Indoor-radon test mean values for the selected formations were also weakly, but statistically significantly, correlated with mean aeroradiometric uranium concentrations. To produce a map useful to nonspecialists, we classified each of the 106 formations into five radon-geologic classes on the basis of their 75th percentile radon concentrations. The statewide map is freely available through an interactive internet map service
Respirable crystalline silica and lung cancer in community-based studies: impact of job-exposure matrix specifications on exposure–response relationships
Objectives: The quantitative job-exposure matrix SYN-JEM consists of various dimensions: job-specific estimates, region-specific estimates, and prior expert ratings of jobs by the semi-quantitative DOM-JEM. We analyzed the effect of different JEM dimensions on the exposure-response relationships between occupational silica exposure and lung cancer risk to investigate how these variations influence estimates of exposure by a quantitative JEM and associated health endpoints. Methods: Using SYN-JEM, and alternative SYN-JEM specifications with varying dimensions included, cumulative silica exposure estimates were assigned to 16 901 lung cancer cases and 20 965 controls pooled from 14 international community-based case-control studies. Exposure-response relationships based on SYN-JEM and alternative SYN-JEM specifications were analyzed using regression analyses (by quartiles and log-transformed continuous silica exposure) and generalized additive models (GAM), adjusted for age, sex, study, cigarette pack-years, time since quitting smoking, and ever employment in occupations with established lung cancer risk. Results: SYN-JEM and alternative specifications generated overall elevated and similar lung cancer odds ratios ranging from 1.13 (1st quartile) to 1.50 (4th quartile). In the categorical and log-linear analyses SYN-JEM with all dimensions included yielded the best model fit, and exclusion of job-specific estimates from SYN-JEM yielded the poorest model fit. Additionally, GAM showed the poorest model fit when excluding job-specific estimates. Conclusion: The established exposure-response relationship between occupational silica exposure and lung cancer was marginally influenced by varying the dimensions of SYN-JEM. Optimized modelling of exposure-response relationships will be obtained when incorporating all relevant dimensions, namely prior rating, job, time, and region. Quantitative job-specific estimates appeared to be the most prominent dimension for this general population JEM
Area deprivation and its association with health in a cross-sectional study: are the results biased by recent migration?
<p>Abstract</p> <p>Background</p> <p>The association between area deprivation and health has mostly been examined in cross-sectional studies or prospective studies with short follow-up. These studies have rarely taken migration into account. This is a possible source of misclassification of exposure, i.e. an unknown number of study participants are attributed an exposure of area deprivation that they may have experienced too short for it to have any influence. The aim of this article was to examine to what extent associations between area deprivation and health outcomes were biased by recent migration.</p> <p>Methods</p> <p>Based on data from the Oslo Health Study, a cross-sectional study conducted in 2000 in Oslo, Norway, we used six health outcomes (self rated health, mental health, coronary heart disease, chronic obstructive pulmonary disease, smoking and exercise) and considered migration nine years prior to the study conduct. Migration into Oslo, between the areas of Oslo, and the changes in area deprivation during the period were taken into account. Associations were investigated by multilevel logistic regression analyses.</p> <p>Results</p> <p>After adjustment for individual socio-demographic variables we found significant associations between area deprivation and all health outcomes. Accounting for migration into Oslo and between areas of Oslo did not change these associations much. However, the people who migrated into Oslo were younger and had lower prevalences of unfavourable health outcomes than those who were already living in Oslo. But since they were evenly distributed across the area deprivation quintiles, they had little influence on the associations between area deprivation and health. Evidence of selective migration within Oslo was weak, as both moving up and down in the deprivation hierarchy was associated with significantly worse health than not moving.</p> <p>Conclusion</p> <p>We have documented significant associations between area deprivation and health outcomes in Oslo after adjustment for socio-demographic variables in a cross-sectional study. These associations were weakly biased by recent migration. From our results it still appears that migration prior to study conduct may be relevant to investigate even within a relatively short period of time, whereas changes in area deprivation during such a period is of limited interest.</p
The "Persuadable Middle" on Same-Sex Marriage: Formative Research to Build Support among Heterosexual College Students
Same-sex marriage is a controversial policy issue that affects the welfare of gay and lesbian couples throughout the USA. Considerable research examines opinions about same-sex marriage; however, studies have not investigated the covariates of the “persuadable middle”— those individuals who are neutral or unsure about their views. This group of people is often the target of same-sex marriage campaigns, yet they have received no empirical attention.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/89607/1/Woodford et al 2011 Persuadable Middle.pd
Detection of diffuse and specular interface reflections and inter-reflections by color image segmentation
We present a computational model and algorithm for detecting diffuse and specular interface reflections and some inter-reflections. Our color reflection model is based on the dichromatic model for dielectric materials and on a color space, called S space, formed with three orthogonal basis functions. We transform color pixels measured in RGB into the S space and analyze color variations on objects in terms of brightness, hue and saturation which are defined in the S space. When transforming the original RGB data into the S space, we discount the scene illumination color that is estimated using a white reference plate as an active probe. As a result, the color image appears as if the scene illumination is white. Under the whitened illumination, the interface reflection clusters in the S space are all aligned with the brightness direction. The brightness, hue and saturation values exhibit a more direct correspondence to body colors and to diffuse and specular interface reflections, shading, shadows and inter-reflections than the RGB coordinates. We exploit these relationships to segment the color image, and to separate specular and diffuse interface reflections and some inter-reflections from body reflections. The proposed algorithm is effications for uniformly colored dielectric surfaces under singly colored scene illumination. Experimental results conform to our model and algorithm within the liminations discussed.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/41303/1/11263_2004_Article_BF00128233.pd
Respirable crystalline silica and lung cancer in community-based studies: impact of job-exposure matrix specifications on exposure-response relationships
OBJECTIVES: The quantitative job-exposure matrix SYN-JEM consists of various dimensions: job-specific estimates, region-specific estimates, and prior expert ratings of jobs by the semi-quantitative DOM-JEM. We analyzed the effect of different JEM dimensions on the exposure-response relationships between occupational silica exposure and lung cancer risk to investigate how these variations influence estimates of exposure by a quantitative JEM and associated health endpoints. METHODS: Using SYN-JEM, and alternative SYN-JEM specifications with varying dimensions included, cumulative silica exposure estimates were assigned to 16 901 lung cancer cases and 20 965 controls pooled from 14 international community-based case-control studies. Exposure-response relationships based on SYN-JEM and alternative SYN-JEM specifications were analyzed using regression analyses (by quartiles and log-transformed continuous silica exposure) and generalized additive models (GAM), adjusted for age, sex, study, cigarette pack-years, time since quitting smoking, and ever employment in occupations with established lung cancer risk. RESULTS: SYN-JEM and alternative specifications generated overall elevated and similar lung cancer odds ratios ranging from 1.13 (1st quartile) to 1.50 (4th quartile). In the categorical and log-linear analyses SYN-JEM with all dimensions included yielded the best model fit, and exclusion of job-specific estimates from SYN-JEM yielded the poorest model fit. Additionally, GAM showed the poorest model fit when excluding job-specific estimates. CONCLUSION: The established exposure-response relationship between occupational silica exposure and lung cancer was marginally influenced by varying the dimensions of SYN-JEM. Optimized modelling of exposure-response relationships will be obtained when incorporating all relevant dimensions, namely prior rating, job, time, and region. Quantitative job-specific estimates appeared to be the most prominent dimension for this general population JEM
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