Abstract: In this study, we propose a tongue diagnosis method
which detects the tongue from face image and divides the tongue area into six areas, and finally generates tongue coating ratio of each area.
To detect the tongue area from face image, we use ASM as one of the active shape models. Detected tongue area is divided into six areas
widely used in the Korean traditional medicine and the distribution of tongue coating of the six areas is examined by SVM(Support Vector
Machine). For SVM, we use a 3-dimensional vector calculated by PCA(Principal Component Analysis) from a 12-dimentional vector
consisting of RGB, HIS, Lab, and Luv. As a result, we detected the tongue area stably using ASM and found that PCA and SVM helped
raise the ratio of tongue coating detection.
Abstract: Cluster analysis is the name given to a diverse collection of techniques that can be used to classify objects (e.g. individuals, quadrats, species etc). While Kohonen's Self-Organizing Feature Map (SOFM) or Self-Organizing Map (SOM) networks have been successfully applied as a classification tool to various problem domains, including speech recognition, image data compression, image or character recognition, robot control and medical diagnosis, its potential as a robust substitute for clustering analysis remains relatively unresearched. SOM networks combine competitive learning with dimensionality reduction by smoothing the clusters with respect to an a priori grid and provide a powerful tool for data visualization. In this paper, SOM is used for creating a toroidal mapping of two-dimensional lattice to perform cluster analysis on results of a chemical analysis of wines produced in the same region in Italy but derived from three different cultivators, referred to as the “wine recognition data" located in the University of California-Irvine database. The results are encouraging and it is believed that SOM would make an appealing and powerful decision-support system tool for clustering tasks and for data visualization.
Abstract: The development of aid's systems for the medical
diagnosis is not easy thing because of presence of inhomogeneities in
the MRI, the variability of the data from a sequence to the other as
well as of other different source distortions that accentuate this
difficulty. A new automatic, contextual, adaptive and robust
segmentation procedure by MRI brain tissue classification is
described in this article. A first phase consists in estimating the
density of probability of the data by the Parzen-Rozenblatt method.
The classification procedure is completely automatic and doesn't
make any assumptions nor on the clusters number nor on the
prototypes of these clusters since these last are detected in an
automatic manner by an operator of mathematical morphology called
skeleton by influence zones detection (SKIZ). The problem of
initialization of the prototypes as well as their number is transformed
in an optimization problem; in more the procedure is adaptive since it
takes in consideration the contextual information presents in every
voxel by an adaptive and robust non parametric model by the
Markov fields (MF). The number of bad classifications is reduced by
the use of the criteria of MPM minimization (Maximum Posterior
Marginal).
Abstract: Advances in clinical medical imaging have brought about the routine production of vast numbers of medical images that need to be analyzed. As a result an enormous amount of computer vision research effort has been targeted at achieving automated medical image analysis. Computed Tomography (CT) is highly accurate for diagnosing liver tumors. This study aimed to evaluate the potential role of the wavelet and the neural network in the differential diagnosis of liver tumors in CT images. The tumors considered in this study are hepatocellular carcinoma, cholangio carcinoma, hemangeoma and hepatoadenoma. Each suspicious tumor region was automatically extracted from the CT abdominal images and the textural information obtained was used to train the Probabilistic Neural Network (PNN) to classify the tumors. Results obtained were evaluated with the help of radiologists. The system differentiates the tumor with relatively high accuracy and is therefore clinically useful.
Abstract: Mammography is the most effective procedure for an
early diagnosis of the breast cancer. Nowadays, people are trying to
find a way or method to support as much as possible to the
radiologists in diagnosis process. The most popular way is now being
developed is using Computer-Aided Detection (CAD) system to
process the digital mammograms and prompt the suspicious region to
radiologist. In this paper, an automated CAD system for detection
and classification of massive lesions in mammographic images is
presented. The system consists of three processing steps: Regions-Of-
Interest detection, feature extraction and classification. Our CAD
system was evaluated on Mini-MIAS database consisting 322
digitalized mammograms. The CAD system-s performance is
evaluated using Receiver Operating Characteristics (ROC) and Freeresponse
ROC (FROC) curves. The archived results are 3.47 false
positives per image (FPpI) and sensitivity of 85%.
Abstract: In this paper we use data mining techniques to investigate factors that contribute significantly to enhancing the risk of acute coronary syndrome. We assume that the dependent variable is diagnosis – with dichotomous values showing presence or absence of disease. We have applied binary regression to the factors affecting the dependent variable. The data set has been taken from two different cardiac hospitals of Karachi, Pakistan. We have total sixteen variables out of which one is assumed dependent and other 15 are independent variables. For better performance of the regression model in predicting acute coronary syndrome, data reduction techniques like principle component analysis is applied. Based on results of data reduction, we have considered only 14 out of sixteen factors.
Abstract: This work proposes an optical fiber system (OF) for
sensing various volatile organic compounds (VOCs) in human breath
for the diagnosis of some metabolic disorders as a non-invasive
methodology. The analyzed VOCs are alkanes (i.e., ethane, pentane,
heptane, octane, and decane), and aromatic compounds (i.e., benzene,
toluene, and styrene). The OF displays high analytical performance
since it provides near real-time responses, rapid analysis, and low
instrumentation costs, as well as it exhibits useful linear range and
detection limits; the developed OF sensor is also comparable to a
reference methodology (gas chromatography-mass spectrometry) for
the eight tested VOCs.
Abstract: This paper introduces an automatic voice classification
system for the diagnosis of individual constitution based on Sasang
Constitutional Medicine (SCM) in Traditional Korean Medicine
(TKM). For the developing of this algorithm, we used the voices of
309 female speakers and extracted a total of 134 speech features from
the voice data consisting of 5 sustained vowels and one sentence. The
classification system, based on a rule-based algorithm that is derived
from a non parametric statistical method, presents 3 types of decisions:
reserved, positive and negative decisions. In conclusion, 71.5% of the
voice data were diagnosed by this system, of which 47.7% were
correct positive decisions and 69.7% were correct negative decisions.
Abstract: Thailand-s health system is challenged by the rising
number of patients and decreasing ratio of medical
practitioners/patients, especially in rural areas. This may tempt
inexperienced GPs to rush through the process of anamnesis with the
risk of incorrect diagnosis. Patients have to travel far to the hospital
and wait for a long time presenting their case. Many patients try to
cure themselves with traditional Thai medicine. Many countries are
making use of the Internet for medical information gathering,
distribution and storage. Telemedicine applications are a relatively
new field of study in Thailand; the infrastructure of ICT had
hampered widespread use of the Internet for using medical
information. With recent improvements made health and technology
professionals can work out novel applications and systems to help
advance telemedicine for the benefit of the people. Here we explore
the use of telemedicine for people with health problems in rural areas
in Thailand and present a Telemedicine Diagnosis System for Rural
Thailand (TEDIST) for diagnosing certain conditions that people
with Internet access can use to establish contact with Community
Health Centers, e.g. by mobile phone. The system uses a Web-based
input method for individual patients- symptoms, which are taken by
an expert system for the analysis of conditions and appropriate
diseases. The analysis harnesses a knowledge base and a backward
chaining component to find out, which health professionals should be
presented with the case. Doctors have the opportunity to exchange
emails or chat with the patients they are responsible for or other
specialists. Patients- data are then stored in a Personal Health Record.
Abstract: In working mode some unexpected changes could
be arise in inner structure of electromagnetic device. They
influence modification in electromagnetic field propagation map.
The field values at an observed boundary are also changed. The
development of the process has to be watched because the arising
structural changes would provoke the device to be gone out later.
The probabilistic assessment of the state is possible to be made.
The numerical assessment points if the resulting changes have
only accidental character or they are due to the essential inner
structural disturbances.
The presented application example is referring to the 200MW
turbine-generator. A part of the stator core end teeth zone is
simulated broken. Quasi three-dimensional electromagnetic and
temperature field are solved applying FEM. The stator core state
diagnosis is proposed to be solved as an identification problem on
the basis of a statistical criterion.
Abstract: Condition monitoring of electrical power equipment
has attracted considerable attention for many years. The aim of this
paper is to use Labview with Fuzzy Logic controller to build a
simulation system to diagnose transformer faults and monitor its
condition. The front panel of the system was designed using
LabVIEW to enable computer to act as customer-designed
instrument. The dissolved gas-in-oil analysis (DGA) method was
used as technique for oil type transformer diagnosis; meanwhile
terminal voltages and currents analysis method was used for dry type
transformer. Fuzzy Logic was used as expert system that assesses all
information keyed in at the front panel to diagnose and predict the
condition of the transformer. The outcome of the Fuzzy Logic
interpretation will be displayed at front panel of LabVIEW to show
the user the conditions of the transformer at any time.
Abstract: Diagnosis can be achieved by building a model of a
certain organ under surveillance and comparing it with the real time
physiological measurements taken from the patient. This paper deals
with the presentation of the benefits of using Data Mining techniques
in the computer-aided diagnosis (CAD), focusing on the cancer
detection, in order to help doctors to make optimal decisions quickly
and accurately. In the field of the noninvasive diagnosis techniques,
the endoscopic ultrasound elastography (EUSE) is a recent elasticity
imaging technique, allowing characterizing the difference between
malignant and benign tumors. Digitalizing and summarizing the main
EUSE sample movies features in a vector form concern with the use
of the exploratory data analysis (EDA). Neural networks are then
trained on the corresponding EUSE sample movies vector input in
such a way that these intelligent systems are able to offer a very
precise and objective diagnosis, discriminating between benign and
malignant tumors. A concrete application of these Data Mining
techniques illustrates the suitability and the reliability of this
methodology in CAD.
Abstract: To investigate effect of salt stress on Chlorophyll
fluorescence four cultivars (fong,star,chamran and kharchia) of wheat
(Triticum aestivum) plants subjected to salinity levels ( control,8,12
and 16 dsm-1 ) from one week after emergence to the end of stem
elongation under greenhouse condition . results showed that quantum
yield of photosystem II from light adopted leaves (ΦPSII),
Photochemical quenching (qP) ,quantum yield of dark adopted leaves
(fv/fm) and non photochemical quenching (NPq) were affected by
salt stress . Salinity levels affected photosynthetic rate. Star and fong
cultivars showed minimum and maximum levels of photosynthetic
rate in respectively. Minimum photosynthetic rate differences
between levels of salinity were shown in Kharchia. Shoot dry matter
of all cultivars decreased by increasing salinity levels. Results
showed that non photochemical quenching by salinity levels attribute
to the decreases in shoot dry matter.
Abstract: Hepatocellular carcinoma, also called hepatoma, most
commonly appears in a patient with chronic viral hepatitis. In
patients with a higher suspicion of HCC, such as small or subtle
rising of serum enzymes levels, the best method of diagnosis
involves a CT scan of the abdomen, but only at high cost. The aim of
this study was to increase the ability of the physician to early detect
HCC, using a probabilistic neural network-based approach, in order
to save time and hospital resources.
Abstract: The nonlinear damping behavior is usually ignored in
the design of a miniature moving-coil loudspeaker. But when the
loudspeaker operated in air, the damping parameter varies with the
voice-coil displacement corresponding due to viscous air flow. The
present paper presents an identification model as inverse problem to
identify the nonlinear damping parameter in the lumped parameter
model for the loudspeaker. Theoretical results for the nonlinear
damping are verified by using laser displacement measurement
scanner. These results indicate that the damping parameter has the
greatly different nonlinearity between in air and vacuum. It is believed
that the results of the present work can be applied in diagnosis and
sound quality improvement of a miniature loudspeaker.
Abstract: A new observer based fault detection and diagnosis
scheme for predicting induction motors- faults is proposed in this
paper. Prediction of incipient faults, using different variants of
Kalman filter and their relative performance are evaluated. Only soft
faults are considered for this work. The data generation, filter
convergence issues, hypothesis testing and residue estimates are
addressed. Simulink model is used for data generation and various
types of faults are considered. A comparative assessment of the
estimates of different observers associated with these faults is
included.
Abstract: Currently, slider process of Hard Disk Drive Industry
become more complex, defective diagnosis for yield improvement
becomes more complicated and time-consumed. Manufacturing data
analysis with data mining approach is widely used for solving that
problem. The existing mining approach from combining of the KMean
clustering, the machine oriented Kruskal-Wallis test and the
multivariate chart were applied for defective diagnosis but it is still
be a semiautomatic diagnosis system. This article aims to modify an
algorithm to support an automatic decision for the existing approach.
Based on the research framework, the new approach can do an
automatic diagnosis and help engineer to find out the defective
factors faster than the existing approach about 50%.
Abstract: Data Mining aims at discovering knowledge out of
data and presenting it in a form that is easily comprehensible to
humans. One of the useful applications in Egypt is the Cancer
management, especially the management of Acute Lymphoblastic
Leukemia or ALL, which is the most common type of cancer in
children.
This paper discusses the process of designing a prototype that can
help in the management of childhood ALL, which has a great
significance in the health care field. Besides, it has a social impact
on decreasing the rate of infection in children in Egypt. It also
provides valubale information about the distribution and
segmentation of ALL in Egypt, which may be linked to the possible
risk factors.
Undirected Knowledge Discovery is used since, in the case of this
research project, there is no target field as the data provided is
mainly subjective. This is done in order to quantify the subjective
variables. Therefore, the computer will be asked to identify
significant patterns in the provided medical data about ALL. This
may be achieved through collecting the data necessary for the
system, determimng the data mining technique to be used for the
system, and choosing the most suitable implementation tool for the
domain.
The research makes use of a data mining tool, Clementine, so as to
apply Decision Trees technique. We feed it with data extracted from
real-life cases taken from specialized Cancer Institutes. Relevant
medical cases details such as patient medical history and diagnosis
are analyzed, classified, and clustered in order to improve the disease
management.
Abstract: Wavelet transform has been extensively used in
machine fault diagnosis and prognosis owing to its strength to deal
with non-stationary signals. The existing Wavelet transform based
schemes for fault diagnosis employ wavelet decomposition of the
entire vibration frequency which not only involve huge
computational overhead in extracting the features but also increases
the dimensionality of the feature vector. This increase in the
dimensionality has the tendency to 'over-fit' the training data and
could mislead the fault diagnostic model. In this paper a novel
technique, envelope wavelet packet transform (EWPT) is proposed in
which features are extracted based on wavelet packet transform of the
filtered envelope signal rather than the overall vibration signal. It not
only reduces the computational overhead in terms of reduced number
of wavelet decomposition levels and features but also improves the
fault detection accuracy. Analytical expressions are provided for the
optimal frequency resolution and decomposition level selection in
EWPT. Experimental results with both actual and simulated machine
fault data demonstrate significant gain in fault detection ability by
EWPT at reduced complexity compared to existing techniques.
Abstract: Atrial Fibrillation is the most common sustained
arrhythmia encountered by clinicians. Because of the invisible
waveform of atrial fibrillation in atrial activation for human, it is
necessary to develop an automatic diagnosis system. 12-Lead ECG
now is available in hospital and is appropriate for using Independent
Component Analysis to estimate the AA period. In this research, we
also adopt a second-order blind identification approach to transform
the sources extracted by ICA to more precise signal and then we use
frequency domain algorithm to do the classification. In experiment,
we gather a significant result of clinical data.