Automated ventricular mapping with multi-atlas fluid image alignment reveals genetic effects in Alzheimer's disease

We developed and validated a new method to create automated 3D parametric surface models of the lateral ventricles in brain MRI scans, providing an efficient approach to monitor degenerative disease in clinical studies and drug trials. First, we used a set of parameterized surfaces to represent the ventricles in four subjects' manually labeled brain MRI scans (atlases). We fluidly registered each atlas and mesh model to MRIs from 17 Alzheimer's disease (AD) patients and 13 age- and gender-matched healthy elderly control subjects, and 18 asymptomatic ApoE4-carriers and 18 age- and gender-matched non-carriers. We examined genotyped healthy subjects with the goal of detecting subtle effects of a gene that confers heightened risk for Alzheimer's disease. We averaged the meshes extracted for each 3D MR data set, and combined the automated segmentations with a radial mapping approach to localize ventricular shape differences in patients. Validation experiments comparing automated and expert manual segmentations showed that (1) the Hausdorff labeling error rapidly decreased, and (2) the power to detect disease- and gene-related alterations improved, as the number of atlases, N, was increased from 1 to 9. In surface-based statistical maps, we detected more widespread and intense anatomical deficits as we increased the number of atlases. We formulated a statistical stopping criterion to determine the optimal number of atlases to use. Healthy ApoE4-carriers and those with AD showed local ventricular abnormalities. This high-throughput method for morphometric studies further motivates the combination of genetic and neuroimaging strategies in predicting AD progression and treatment response. © 2007 Elsevier Inc. All rights reserved.

MR image-based measurement of rates of change in volumes of brain structures. Part II: Application to a study of Alzheimer's disease and normal aging

We present global and regional rates of brain atrophy measured on serially acquired Tl-weighted brain MR images for a group of Alzheimer's disease (AD) patients and age-matched normal control (NC) subjects using the analysis procedure described in Part I. Three rates of brain atrophy: the rate of atrophy in the cerebrum, the rate of lateral ventricular enlargement and the rate of atrophy in the region of temporal lobes, were evaluated for 14 AD patients and 14 age-matched NC subjects. All three rates showed significant differences between the two groups. However, the greatest separation of the two groups was obtained when the regional rates were combined. This application has demonstrated that rates of brain atrophy, especially in specific regions of the brain, based on MR images can provide sensitive measures for evaluating the progression of AD. These measures will be useful for the evaluation of therapeutic effects of novel therapies for AD.

Birdcall retrieval from environmental acoustic recordings using image processing

Acoustic recordings of the environment provide an effective means to monitor bird species diversity. To facilitate exploration of acoustic recordings, we describe a content-based birdcall retrieval algorithm. A query birdcall is a region of spectrogram bounded by frequency and time. Retrieval depends on a similarity measure derived from the orientation and distribution of spectral ridges. The spectral ridge detection method caters for a broad range of birdcall structures. In this paper, we extend previous work by incorporating a spectrogram scaling step in order to improve the detection of spectral ridges. Compared to an existing approach based on MFCC features, our feature representation achieves better retrieval performance for multiple bird species in noisy recordings.

Scalable Bayesian Computation for intractable likelihoods in image analysis

The inverse temperature hyperparameter of the hidden Potts model governs the strength of spatial cohesion and therefore has a substantial influence over the resulting model fit. The difficulty arises from the dependence of an intractable normalising constant on the value of the inverse temperature, thus there is no closed form solution for sampling from the distribution directly. We review three computational approaches for addressing this issue, namely pseudolikelihood, path sampling, and the approximate exchange algorithm. We compare the accuracy and scalability of these methods using a simulation study.

Evaluation of a mobile phone image-based dietary assessment method in adults with type 2 diabetes

Photographic and image-based dietary records have limited evidence evaluating their performance and use among adults with a chronic disease. This study evaluated the performance of a mobile phone image-based dietary record, the Nutricam Dietary Assessment Method (NuDAM), in adults with type 2 diabetes mellitus (T2DM). Criterion validity was determined by comparing energy intake (EI) with total energy expenditure (TEE) measured by the doubly-labelled water technique. Relative validity was established by comparison to a weighed food record (WFR). Inter-rater reliability was assessed by comparing estimates of intake from three dietitians. Ten adults (6 males, age=61.2±6.9 years, BMI=31.0±4.5 kg/m2) participated. Compared to TEE, mean EI was under-reported using both methods, with a mean ratio of EI:TEE 0.76±0.20 for the NuDAM and 0.76±0.17 for the WFR. There was moderate to high correlations between the NuDAM and WFR for energy (r=0.57), carbohydrate (r=0.63, p<0.05), protein (r=0.78, p<0.01) and alcohol (rs=0.85, p<0.01), with a weaker relationship for fat (r=0.24). Agreement between dietitians for nutrient intake for the 3-day NuDAM (ICC = 0.77-0.99) was marginally lower when compared with the 3-day WFR (ICC=0.82-0.99). All subjects preferred using the NuDAM and were willing to use it again for longer recording periods.

No-reference image quality assessment using modified extreme learning machine classifier

In this paper, we present a machine learning approach to measure the visual quality of JPEG-coded images. The features for predicting the perceived image quality are extracted by considering key human visual sensitivity (HVS) factors such as edge amplitude, edge length, background activity and background luminance. Image quality assessment involves estimating the functional relationship between HVS features and subjective test scores. The quality of the compressed images are obtained without referring to their original images ('No Reference' metric). Here, the problem of quality estimation is transformed to a classification problem and solved using extreme learning machine (ELM) algorithm. In ELM, the input weights and the bias values are randomly chosen and the output weights are analytically calculated. The generalization performance of the ELM algorithm for classification problems with imbalance in the number of samples per quality class depends critically on the input weights and the bias values. Hence, we propose two schemes, namely the k-fold selection scheme (KS-ELM) and the real-coded genetic algorithm (RCGA-ELM) to select the input weights and the bias values such that the generalization performance of the classifier is a maximum. Results indicate that the proposed schemes significantly improve the performance of ELM classifier under imbalance condition for image quality assessment. The experimental results prove that the estimated visual quality of the proposed RCGA-ELM emulates the mean opinion score very well. The experimental results are compared with the existing JPEG no-reference image quality metric and full-reference structural similarity image quality metric.

Identity, image, and sexuality in young adults with cancer

The impact of disease and treatment on a young adult's self-image and sexuality has been largely overlooked. This is surprising given that establishing social and romantic relationships is a normal occurrence in young adulthood. This article describes three female patients' cancer journeys and demonstrates how their experiences have impacted their psychosocial function and self-regard. The themes of body image, self-esteem, and identity formation are explored, in relation to implications for relationship-building and moving beyond a cancer diagnosis. This article has been written by young cancer survivors, Danielle Tindle, Kelly Denver, and Faye Lilley, in an effort to elucidate the ongoing struggle to reconcile cancer into a normal young adult's life.

Image processing and classification procedure for the analysis of Australian frog vocalisations

Frog species have been declining worldwide at unprecedented rates in the past decades. There are many reasons for this decline including pollution, habitat loss, and invasive species [1]. To preserve, protect, and restore frog biodiversity, it is important to monitor and assess frog species. In this paper, a novel method using image processing techniques for analyzing Australian frog vocalisations is proposed. An FFT is applied to audio data to produce a spectrogram. Then, acoustic events are detected and isolated into corresponding segments through image processing techniques applied to the spectrogram. For each segment, spectral peak tracks are extracted with selected seeds and a region growing technique is utilised to obtain the contour of each frog vocalisation. Based on spectral peak tracks and the contour of each frog vocalisation, six feature sets are extracted. Principal component analysis reduces each feature set down to six principal components which are tested for classification performance with a k-nearest neighbor classifier. This experiment tests the proposed method of classification on fourteen frog species which are geographically well distributed throughout Queensland, Australia. The experimental results show that the best average classification accuracy for the fourteen frog species can be up to 87%.

Application of image processing techniques for frog call classification

Frogs have received increasing attention due to their effectiveness for indicating the environment change. Therefore, it is important to monitor and assess frogs. With the development of sensor techniques, large volumes of audio data (including frog calls) have been collected and need to be analysed. After transforming the audio data into its spectrogram representation using short-time Fourier transform, the visual inspection of this representation motivates us to use image processing techniques for analysing audio data. Applying acoustic event detection (AED) method to spectrograms, acoustic events are firstly detected from which ridges are extracted. Three feature sets, Mel-frequency cepstral coefficients (MFCCs), AED feature set and ridge feature set, are then used for frog call classification with a support vector machine classifier. Fifteen frog species widely spread in Queensland, Australia, are selected to evaluate the proposed method. The experimental results show that ridge feature set can achieve an average classification accuracy of 74.73% which outperforms the MFCCs (38.99%) and AED feature set (67.78%).

The hunt for disability: The new eugenics and the normalization of school children

Examines issues of sameness, difference, equality, and democracy in present public school systems, focusing on the question of (dis)ability and implications of rethinking (dis)ability as an ontological issue before its inscription as an educational one concerning the politics of inclusion. The paper analyzes old and new discourses of eugenics as quality control of national populations. (Contains references.) (SM)

Crop Classification using Biologically-inspired Techniques with High Resolution Satellite Image

Remote sensing provides a lucid and effective means for crop coverage identification. Crop coverage identification is a very important technique, as it provides vital information on the type and extent of crop cultivated in a particular area. This information has immense potential in the planning for further cultivation activities and for optimal usage of the available fertile land. As the frontiers of space technology advance, the knowledge derived from the satellite data has also grown in sophistication. Further, image classification forms the core of the solution to the crop coverage identification problem. No single classifier can prove to satisfactorily classify all the basic crop cover mapping problems of a cultivated region. We present in this paper the experimental results of multiple classification techniques for the problem of crop cover mapping of a cultivated region. A detailed comparison of the algorithms inspired by social behaviour of insects and conventional statistical method for crop classification is presented in this paper. These include the Maximum Likelihood Classifier (MLC), Particle Swarm Optimisation (PSO) and Ant Colony Optimisation (ACO) techniques. The high resolution satellite image has been used for the experiments.

Image-based midsole insert design and the material effects on heel plantar pressure distribution during simulated walking loads

The primary objective of this paper is to study the use of medical image-based finite element (FE) modelling in subjectspecific midsole design and optimisation for heel pressure reduction using a midsole plug under the calcaneus area (UCA). Plugs with different relative dimensions to the size of the calcaneus of the subject have been incorporated in the heel region of the midsole. The FE foot model was validated by comparing the numerically predicted plantar pressure with biomechanical tests conducted on the same subject. For each UCA midsole plug design, the effect of material properties and plug thicknesses on the plantar pressure distribution and peak pressure level during the heel strike phase of normal walking was systematically studied. The results showed that the UCA midsole insert could effectively modify the pressure distribution, and its effect is directly associated with the ratio of the plug dimension to the size of the calcaneus bone of the subject. A medium hardness plug with a size of 95% of the calcaneus has achieved the best performance for relieving the peak pressure in comparison with the pressure level for a solid midsole without a plug, whereas a smaller plug with a size of 65% of the calcaneus insert with a very soft material showed minimum beneficial effect for the pressure relief.

Drops That Open Worlds : Image of Water in the Poetry of Euphrase Kezilahabi

Euphrase Kezilahabi on tansanialainen kirjailija, joka ensimmäisenä julkaisi swahilinkielisen vapaalla mitalla kirjoitetun runokokoelman. Perinteisessä swahilirunoudessa tiukat muotosäännöt ovat tärkeitä, ja teos synnytti kiivasta keskustelua. Runoteokset Kichomi ( Viilto , Kipu , 1974) ja Karibu Ndani ( Tervetuloa sisään , 1988) sekä Kezilahabin muu tuotanto voidaan nähdä uuden sukupolven taiteena. Kezilahabi on arvostettu runoilija, mutta hänen runojaan ei aiemmin ole käännetty englanniksi (yksittäisiä säkeitä lukuunottamatta), eikä juurikaan tutkittu yksityiskohtaisesti. Yleiskuvaan pyrkivissä lausunnoissa Kezilahabin runouden on hyvin usein määritelty olevan poliittista. Monet Kezilahabin runoista ottavatkin kantaa yhteiskunnallisiin kysymyksiin, mutta niiden pohdinta on kuitenkin runoissa vain yksi taso. Sen lisäksi Kezilahabin lyriikassa on paljon muuta ennen kartoittamatonta tämä tutkimus keskittyy veden kuvaan (the image of water). Kezilahabi vietti lapsuutensa saarella Victoria-järven keskellä, ja hänen vesikuvastonsa on rikasta. Tutkimuskysymyksenä on, mitä veden kuva runoteoksissa Kichomi ja Karibu Ndani esittää. Runojen analysoinnissa ja tulkinnassa on tarkasteltu myös sitä, miten äänteellinen taso osallistuu kuvien luomiseen. Tutkimuksen määritelmä kuvasta pohjautuu osittain Hugh Kennerin näkemykseen, jonka mukaan oleellista kuvassa on kirjaimellinen taso. Kennerin lähtökohtaan on yhdistetty John Shoptawin teoriaa, joka korostaa runon äänteellisen puolen tärkeyttä merkityksen muodostumisessa. Foneemien analyysissä vaikutteena on ollut Reuven Tsurin teoria. Analyysiosio osoittaa, että veden kuva edustaa ja käsittelee teoksissa lukuisia teemoja: elämää, kuolemaa, fyysistä vetovoimaa, runoutta, mielikuvitusta ja (ali)tajuntaa sekä moraalia. Veden kuvan tutkimuksen pohjalta on nähtävissä, että Kezilahabin filosofia asettuu elävä/kuollut- ja elämä/kuolema dikotomioiden ulkopuolelle.

Solution conformations of penta and heptapeptides containing repetitive α-aminoisobutyryl-Image -alanyl and α -aminoisobutyryl-Image -valyl sequences

The presence of folded solution conformations in the peptides Boc-Ala-(Aib-Ala)2-OMe, Boc-Val-(Aib-Val) 2-OMe, Boc-Ala-(Aib-Ala)3-OMe and Boc-Val-(Aib-Val)3-OMe has been established by 270MHz 1H NMR. Intramolecularly H-bonded NH groups have been identified using temperature and solvent dependence of NH chemical shifts and paramagnetic radical induced broadening of NH resonances. Both pentapeptides adopt 310 helical conformations possessing 3 intramolecular H-bonds in CDCl3 and (CD3)2SO. The heptapeptides favour helical structures with 5 H-bonds in CDCl3. In (CD3)2SO only 4 H-bonds are readily detected.

Image reconstruction with laterally truncated projections in helical cone-beam CT: Linear prediction based projection completion techniques

Lateral or transaxial truncation of cone-beam data can occur either due to the field of view limitation of the scanning apparatus or iregion-of-interest tomography. In this paper, we Suggest two new methods to handle lateral truncation in helical scan CT. It is seen that reconstruction with laterally truncated projection data, assuming it to be complete, gives severe artifacts which even penetrates into the field of view. A row-by-row data completion approach using linear prediction is introduced for helical scan truncated data. An extension of this technique known as windowed linear prediction approach is introduced. Efficacy of the two techniques are shown using simulation with standard phantoms. A quantitative image quality measure of the resulting reconstructed images are used to evaluate the performance of the proposed methods against an extension of a standard existing technique.