Abstract: Obesity is frequent attendant phenomenon of patients
with endocrinological disease. Between BMI and endocrinological
diseases is close correlation. In thesis we focused on the allocation of
hormone concentration – PTH and TSH, CHOL a mineral element Ca
in a blood serum. The examined group was formed by 100
respondents (women) aged 36 – 83 years, who were divided into two
groups – control group (CG), group with diagnosed endocrine disease
(DED). The concentration of PTH and TSH, Ca and CHOL was
measured through the medium of analyzers Cobas e411 (Japan);
Cobas Integra 400 (Switzerland). At individuals was measured body
weight as well as stature and thereupon from those data we
enumerated BMI. On the basis of Student T-test in biochemical
parameter of PTH and Ca we found out significantly meaningful
difference (p
Abstract: The main goal in this paper is to quantify the quality of
different techniques for radiation treatment plans, a back-propagation
artificial neural network (ANN) combined with biomedicine theory
was used to model thirteen dosimetric parameters and to calculate
two dosimetric indices. The correlations between dosimetric indices
and quality of life were extracted as the features and used in the ANN
model to make decisions in the clinic. The simulation results show
that a trained multilayer back-propagation neural network model can
help a doctor accept or reject a plan efficiently. In addition, the
models are flexible and whenever a new treatment technique enters
the market, the feature variables simply need to be imported and the
model re-trained for it to be ready for use.
Abstract: Background noise is particularly damaging to speech
intelligibility for people with hearing loss especially for sensorineural
loss patients. Several investigations on speech intelligibility have
demonstrated sensorineural loss patients need 5-15 dB higher SNR
than the normal hearing subjects. This paper describes Discrete
Cosine Transform Power Normalized Least Mean Square algorithm
to improve the SNR and to reduce the convergence rate of the LMS
for Sensory neural loss patients. Since it requires only real arithmetic,
it establishes the faster convergence rate as compare to time domain
LMS and also this transformation improves the eigenvalue
distribution of the input autocorrelation matrix of the LMS filter.
The DCT has good ortho-normal, separable, and energy compaction
property. Although the DCT does not separate frequencies, it is a
powerful signal decorrelator. It is a real valued function and thus
can be effectively used in real-time operation. The advantages of
DCT-LMS as compared to standard LMS algorithm are shown via
SNR and eigenvalue ratio computations. . Exploiting the symmetry
of the basis functions, the DCT transform matrix [AN] can be
factored into a series of ±1 butterflies and rotation angles. This
factorization results in one of the fastest DCT implementation. There
are different ways to obtain factorizations. This work uses the fast
factored DCT algorithm developed by Chen and company. The
computer simulations results show superior convergence
characteristics of the proposed algorithm by improving the SNR at
least 10 dB for input SNR less than and equal to 0 dB, faster
convergence speed and better time and frequency characteristics.
Abstract: The linear methods of heart rate variability analysis
such as non-parametric (e.g. fast Fourier transform analysis) and
parametric methods (e.g. autoregressive modeling) has become an
established non-invasive tool for marking the cardiac health, but their
sensitivity and specificity were found to be lower than expected with
positive predictive value
Abstract: Pattern recognition is the research area of Artificial Intelligence that studies the operation and design of systems that recognize patterns in the data. Important application areas are image analysis, character recognition, fingerprint classification, speech analysis, DNA sequence identification, man and machine diagnostics, person identification and industrial inspection. The interest in improving the classification systems of data analysis is independent from the context of applications. In fact, in many studies it is often the case to have to recognize and to distinguish groups of various objects, which requires the need for valid instruments capable to perform this task. The objective of this article is to show several methodologies of Artificial Intelligence for data classification applied to biomedical patterns. In particular, this work deals with the realization of a Computer-Aided Detection system (CADe) that is able to assist the radiologist in identifying types of mammary tumor lesions. As an additional biomedical application of the classification systems, we present a study conducted on blood samples which shows how these methods may help to distinguish between carriers of Thalassemia (or Mediterranean Anaemia) and healthy subjects.
Abstract: In this paper, we evaluate the performance of some wavelet based coding algorithms such as 3D QT-L, 3D SPIHT and JPEG2K. In the first step we achieve an objective comparison between three coders, namely 3D SPIHT, 3D QT-L and JPEG2K. For this purpose, eight MRI head scan test sets of 256 x 256x124 voxels have been used. Results show superior performance of 3D SPIHT algorithm, whereas 3D QT-L outperforms JPEG2K. The second step consists of evaluating the robustness of 3D SPIHT and JPEG2K coding algorithm over wireless transmission. Compressed dataset images are then transmitted over AWGN wireless channel or over Rayleigh wireless channel. Results show the superiority of JPEG2K over these two models. In fact, it has been deduced that JPEG2K is more robust regarding coding errors. Thus we may conclude the necessity of using corrector codes in order to protect the transmitted medical information.
Abstract: CT assessment of postoperative spine is challenging in the presence of metal streak artifacts that could deteriorate the
quality of CT images. In this paper, we studied the influence of different acquisition parameters on the magnitude of metal streaking.
A water-bath phantom was constructed with metal insertion similar with postoperative spine assessment. The phantom was scanned with
different acquisition settings and acquired data were reconstructed
using various reconstruction settings. Standardized ROIs were defined within streaking region for image analysis. The result shows
increased kVp and mAs enhanced SNR values by reducing image
noise. Sharper kernel enhanced image quality compared to smooth
kernel, but produced more noise in the images with higher CT fluctuation. The noise between both kernels were significantly
different (P
Abstract: Sensory nerves in the foot play an important part in the diagnosis of various neuropathydisorders, especially in diabetes mellitus.However, a detailed description of the anatomical distribution of the nerves is currently lacking. A computationalmodel of the afferent nerves inthe foot may bea useful tool for the study of diabetic neuropathy. In this study, we present the development of an anatomically-based model of various major sensory nerves of the sole and dorsal sidesof the foot. In addition, we presentan algorithm for generating synthetic somatosensory nerve networks in the big-toe region of a right foot model. The algorithm was based on a modified version of the Monte Carlo algorithm, with the capability of being able to vary the intra-epidermal nerve fiber density in differentregionsof the foot model. Preliminary results from the combinedmodel show the realistic anatomical structure of the major nerves as well as the smaller somatosensory nerves of the foot. The model may now be developed to investigate the functional outcomes of structural neuropathyindiabetic patients.
Abstract: Image processing for capsule endoscopy requires large
memory and it takes hours for diagnosis since operation time is
normally more than 8 hours. A real-time analysis algorithm of capsule
images can be clinically very useful. It can differentiate abnormal
tissue from health structure and provide with correlation information
among the images. Bleeding is our interest in this regard and we
propose a method of detecting frames with potential bleeding in
real-time. Our detection algorithm is based on statistical analysis and
the shapes of bleeding spots. We tested our algorithm with 30 cases of
capsule endoscopy in the digestive track. Results were excellent where
a sensitivity of 99% and a specificity of 97% were achieved in
detecting the image frames with bleeding spots.
Abstract: The application of Neural Network for disease
diagnosis has made great progress and is widely used by physicians.
An Electrocardiogram carries vital information about heart activity and physicians use this signal for cardiac disease diagnosis which
was the great motivation towards our study. In our work, tachycardia
features obtained are used for the training and testing of a Neural
Network. In this study we are using Fuzzy Probabilistic Neural
Networks as an automatic technique for ECG signal analysis. As
every real signal recorded by the equipment can have different
artifacts, we needed to do some preprocessing steps before feeding it
to our system. Wavelet transform is used for extracting the
morphological parameters of the ECG signal. The outcome of the
approach for the variety of arrhythmias shows the represented
approach is superior than prior presented algorithms with an average
accuracy of about %95 for more than 7 tachy arrhythmias.
Abstract: Emerging Bio-engineering fields such as Brain
Computer Interfaces, neuroprothesis devices and modeling and
simulation of neural networks have led to increased research activity
in algorithms for the detection, isolation and classification of Action
Potentials (AP) from noisy data trains. Current techniques in the field
of 'unsupervised no-prior knowledge' biosignal processing include
energy operators, wavelet detection and adaptive thresholding. These
tend to bias towards larger AP waveforms, AP may be missed due to
deviations in spike shape and frequency and correlated noise
spectrums can cause false detection. Also, such algorithms tend to
suffer from large computational expense.
A new signal detection technique based upon the ideas of phasespace
diagrams and trajectories is proposed based upon the use of a
delayed copy of the AP to highlight discontinuities relative to
background noise. This idea has been used to create algorithms that
are computationally inexpensive and address the above problems.
Distinct AP have been picked out and manually classified from
real physiological data recorded from a cockroach. To facilitate
testing of the new technique, an Auto Regressive Moving Average
(ARMA) noise model has been constructed bases upon background
noise of the recordings. Along with the AP classification means this
model enables generation of realistic neuronal data sets at arbitrary
signal to noise ratio (SNR).
Abstract: A two dimensional three segments coupled pendulum system that mathematically models human arm configuration was developed along with constructing and solving the equations of motions for this model using the energy (work) based approach of Lagrange. The equations of motion of the model were solved iteratively both as an initial value problem and as a two point boundary value problem. In the initial value problem solutions, both the initial system configuration (segment angles) and initial system velocity (segment angular velocities) were used as inputs, whereas, in the two point boundary value problem solutions initial and final configurations and time were used as inputs to solve for the trajectory of motion. The results suggest that the model solutions are sensitive to small changes in the dynamic forces applied to the system as well as to the initial and boundary conditions used. To overcome the system sensitivity a new approach is suggested.
Abstract: Accurate assessment of the primary tumor response to
treatment is important in the management of breast cancer. This
paper introduces a new set of treatment evaluation indicators for
breast cancer cases based on the computational process of three
known metrics, the Euclidian, Hamming and Levenshtein distances.
The distance principals are applied to pairs of mammograms and/or
echograms, recorded before and after treatment, determining a
reference point in judging the evolution amount of the studied
carcinoma. The obtained numerical results are indeed very
transparent and indicate not only the evolution or the involution of
the tumor under treatment, but also a quantitative measurement of the
benefit in using the selected method of treatment.
Abstract: Sleep spindles are the most interesting hallmark of
stage 2 sleep EEG. Their accurate identification in a
polysomnographic signal is essential for sleep professionals to help
them mark Stage 2 sleep. Sleep Spindles are also promising objective
indicators for neurodegenerative disorders. Visual spindle scoring
however is a tedious workload. In this paper three different
approaches are used for the automatic detection of sleep spindles:
Short Time Fourier Transform, Wavelet Transform and Wave
Morphology for Spindle Detection. In order to improve the results, a
combination of the three detectors is presented and comparison with
human expert scorers is performed. The best performance is obtained
with a combination of the three algorithms which resulted in a
sensitivity and specificity of 94% when compared to human expert
scorers.
Abstract: A scaffold is necessary for tooth regeneration because of its three-dimensional geometry. For restoration of defect, it is necessary for the scaffold to be prepared in the shape of the defect. Sponges made from polyvinyl alcohol with formalin cross-linking (PVF sponge) have been used for scaffolds for bone formation in vivo. To induce osteogenesis within the sponge, methods of growing rat bone marrow cells (rBMCs) among the fiber structures in the sponge might be considered. Storage of rBMCs among the fibers in the sponge coated with dextran (10 kDa) was tried. After seeding of rBMCs to PVF sponge immersed in dextran solution at 2 g/dl concentration, osteogenesis was recognized in subcutaneously implanted PVF sponge as a scaffold in vivo. The level of osteocalcin was 25.28±5.71 ng/scaffold and that of Ca was 129.20±19.69 µg/scaffold. These values were significantly higher than those in sponges without dextran coating (p
Abstract: Removal of PCP by a system combining
biodegradation by biofilm and adsorption was investigated here.
Three studies were conducted employing batch tests, sequencing
batch reactor (SBR) and continuous biofilm activated carbon
column reactor (BACCOR). The combination of biofilm-GAC
batch process removed about 30% more PCP than GAC adsorption
alone. For the SBR processes, both the suspended and attached
biomass could remove more than 90% of the PCP after
acclimatisation. BACCOR was able to remove more than 98% of
PCP-Na at concentrations ranging from 10 to 100 mg/L, at empty
bed contact time (EBCT) ranging from 0.75 to 4 hours. Pure and
mixed cultures from BACCOR were tested for use of PCP as sole
carbon and energy source under aerobic conditions. The isolates
were able to degrade up to 42% of PCP under aerobic conditions in
pure cultures. However, mixed cultures were found able to degrade
more than 99% PCP indicating interdependence of species.
Abstract: Segmentation is an important step in medical image
analysis and classification for radiological evaluation or computer
aided diagnosis. The CAD (Computer Aided Diagnosis ) of lung CT
generally first segment the area of interest (lung) and then analyze
the separately obtained area for nodule detection in order to
diagnosis the disease. For normal lung, segmentation can be
performed by making use of excellent contrast between air and
surrounding tissues. However this approach fails when lung is
affected by high density pathology. Dense pathologies are present in
approximately a fifth of clinical scans, and for computer analysis
such as detection and quantification of abnormal areas it is vital that
the entire and perfectly lung part of the image is provided and no
part, as present in the original image be eradicated. In this paper we
have proposed a lung segmentation technique which accurately
segment the lung parenchyma from lung CT Scan images. The
algorithm was tested against the 25 datasets of different patients
received from Ackron Univeristy, USA and AGA Khan Medical
University, Karachi, Pakistan.
Abstract: F-actin fibrils are the cytoskeleton of osteocytes. They react in a dynamic manner to mechanical loading, and strength and
reposition their efforts to reinforce the cells structure. We hypothesize that f-actin is temporarly disrupted after loading and repolymerizes
in a new orientation to oppose the applied load. In vitro studies are conducted to determine f-actin disruption after varying mechanical stimulus parameters that are known to affect bone
formation. Results indicate that the f-actin cytoskeleton is disrupted in vitro as a function of applied mechanical stimulus parameters and
that the f-actin bundles reassemble after loading induced disruption
within 3 minutes after cessation of loading. The disruption of the factin
cytoskeleton depends on the magnitude of stretch, the numbers
of loading cycles, frequency, the insertion of rest between loading
cycles and extracellular calcium. In vivo studies also demonstrate
disruption of the f-actin cytoskeleton in cells embedded in the bone
matrix immediately after mechanical loading. These studies suggest
that adaptation of the f-actin fiber bundles of the cytoskeleton in
response to applied loads occurs by disruption and subsequent repolymerization.
Abstract: The human body has a complex system of innate and adaptive mechanisms for combating infection. This article discusses the role and relative effectiveness of these mechanisms in relation to small pox and AIDS.
Abstract: Engineered nanoparticles’ usage rapidly increased in
various applications in the last decade due to their unusual properties.
However, there is an ever increasing concern to understand their
toxicological effect in human health. Particularly, metal and metal
oxide nanoparticles have been used in various sectors including
biomedical, food and agriculture. But their impact on human health is
yet to be fully understood. In this present investigation, we assessed
the toxic effect of engineered nanoparticles (ENPs) including Ag,
MgO and Co3O4 nanoparticles (NPs) on human mesenchymal stem
cells (hMSC) adopting cell viability and cellular morphological
changes as tools The results suggested that silver NPs are more toxic
than MgO and Co3O4NPs. The ENPs induced cytotoxicity and
nuclear morphological changes in hMSC depending on dose. The cell
viability decreases with increase in concentration of ENPs. The
cellular morphology studies revealed that ENPs damaged the cells.
These preliminary findings have implications for the use of these
nanoparticles in food industry with systematic regulations.