Abstract: This paper describes about the process of recognition and classification of brain images such as normal and abnormal based on PSO-SVM. Image Classification is becoming more important for medical diagnosis process. In medical area especially for diagnosis the abnormality of the patient is classified, which plays a great role for the doctors to diagnosis the patient according to the severeness of the diseases. In case of DICOM images it is very tough for optimal recognition and early detection of diseases. Our work focuses on recognition and classification of DICOM image based on collective approach of digital image processing. For optimal recognition and classification Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and Support Vector Machine (SVM) are used. The collective approach by using PSO-SVM gives high approximation capability and much faster convergence.
Abstract: Visual attention allows user to select the most relevant
information to ongoing behaviour. This paper presents a study on; i)
the performance of people measurements, ii) accurateness of people
measurement of the peaks that correspond to chemical quantities
from the Magnetic Resonance Spectroscopy (MRS) graphs and iii)
affects of people measurements to the algorithm-based diagnosis.
Participant-s eye-movement was recorded using eye-tracker tool
(Eyelink II). This experiment involves three participants for
examining 20 MRS graphs to estimate the peaks of chemical
quantities which indicate the abnormalities associated with
Cerebellar Tumours (CT). The status of each MRS is verified by
using decision algorithm. Analysis involves determination of
humans-s eye movement pattern in measuring the peak of
spectrograms, scan path and determining the relationship of
distributions of fixation durations with the accuracy of measurement.
In particular, the eye-tracking data revealed which aspects of the
spectrogram received more visual attention and in what order they
were viewed. This preliminary investigation provides a proof of
concept for use of the eye tracking technology as the basis for
expanded CT diagnosis.
Abstract: Sickle cell anemia is a recessive genetic disease
caused by the presence in the red blood cell, of abnormal hemoglobin
called hemoglobin S. It results from the replacement in the beta chain
of the acid glutamic acid by valin at position 6. Topics may be
homozygous (SS) or heterozygous (AS) most often
asymptomatic. Other mutations result in compound heterozygous:
- Synthesis of hemoglobin C mutation in the sixth leucin codon
(heterozygous SC);
- ß-thalassemia (heterozygous S-ß thalassemia).
SS homozygous, heterozygous SC and S- ß -thalassemia are grouped
under the major sickle cell syndromes.
To make a laboratory diagnosis of hemoglobinopathies in a
portion of the population in region of Batna, our study was
conducted on 115 patients with suspected sickle cell anemia, all cases
have benefited from hematological tests as blood count (count RBC,
calculated erythrocyte indices, MCV and MCHC, measuring the
hemoglobin concentration) and a biochemical test in this case
electrophoresis CAPILLARYS HEMOGLOBIN (E).
The results showed:
27 cases of sickle cell anemia were found on 115 suspected cases,
73,03% homozygous sickle cell disease and 59,25% sickle cell trait.
Finally, the double heterozygous S/C, represent the incidence rate of
3, 70%.
Abstract: Rolling element bearings are widely used in industry,
especially where high load capacity is required. The diagnosis of
their conditions is essential matter for downtime reduction and saving
cost of maintenance. Therefore, an intensive analysis of frequency
spectrum of their faults must be carried out in order to determine the
main reason of the fault. This paper focus on a beating phenomena
observed in the waveform (time domain) of a cylindrical rolling
element bearing. The beating frequencies were not related to any
sources nearby the machine nor any other malfunctions (unbalance,
misalignment ...etc). More investigation on the spike energy and the
frequency spectrum indicated a problem with races of the bearing.
Multi-harmonics of the fundamental defects frequencies were
observed. Two of them were close to each other in magnitude those
were the source of the beating phenomena.
Abstract: We propose a method for discrimination and
classification of ovarian with benign, malignant and normal tissue
using independent component analysis and neural networks. The
method was tested for a proteomic patters set from A database, and
radial basis functions neural networks. The best performance was
obtained with probabilistic neural networks, resulting I 99% success
rate, with 98% of specificity e 100% of sensitivity.
Abstract: Image registration plays an important role in the
diagnosis of dental pathologies such as dental caries, alveolar bone
loss and periapical lesions etc. This paper presents a new wavelet
based algorithm for registering noisy and poor contrast dental x-rays.
Proposed algorithm has two stages. First stage is a preprocessing
stage, removes the noise from the x-ray images. Gaussian filter has
been used. Second stage is a geometric transformation stage.
Proposed work uses two levels of affine transformation. Wavelet
coefficients are correlated instead of gray values. Algorithm has been
applied on number of pre and post RCT (Root canal treatment)
periapical radiographs. Root Mean Square Error (RMSE) and
Correlation coefficients (CC) are used for quantitative evaluation.
Proposed technique outperforms conventional Multiresolution
strategy based image registration technique and manual registration
technique.
Abstract: Early detection of breast cancer is considered as a
major public health issue. Breast cancer screening is not generalized
to the entire population due to a lack of resources, staff and
appropriate tools. Systematic screening can result in a volume of data
which can not be managed by present computer architecture, either in
terms of storage capabilities or in terms of exploitation tools. We
propose in this paper to design and develop a data warehouse system
in radiology-senology (DWRS). The aim of such a system is on one
hand, to support this important volume of information providing from
multiple sources of data and images and for the other hand, to help
assist breast cancer screening in diagnosis, education and research.
Abstract: In this paper, we propose disease diagnosis hardware
architecture by using Hypernetworks technique. It can be used to
diagnose 3 different diseases (SPECT Heart, Leukemia, Prostate
cancer). Generally, the disparate diseases require specified diagnosis
hardware model for each disease. Using similarities of three diseases
diagnosis processor, we design diagnosis processor that can diagnose
three different diseases. Our proposed architecture that is combining
three processors to one processor can reduce hardware size without
decrease of the accuracy.
Abstract: This paper presents an integrated case based and rule
based reasoning method for car faulty diagnosis. The reasoning
method is done through extracting the past cases from the Proton
Service Center while comparing with the preset rules to deduce a
diagnosis/solution to a car service case. New cases will be stored to
the knowledge base. The test cases examples illustrate the
effectiveness of the proposed integrated reasoning. It has proven
accuracy of similar reasoning if carried out by a service advisor from
the service center.
Abstract: In order to maximize efficiency of an information management platform and to assist in decision making, the collection, storage and analysis of performance-relevant data has become of fundamental importance. This paper addresses the merits and drawbacks provided by the OLAP paradigm for efficiently navigating large volumes of performance measurement data hierarchically. The system managers or database administrators navigate through adequately (re)structured measurement data aiming to detect performance bottlenecks, identify causes for performance problems or assessing the impact of configuration changes on the system and its representative metrics. Of particular importance is finding the root cause of an imminent problem, threatening availability and performance of an information system. Leveraging OLAP techniques, in contrast to traditional static reporting, this is supposed to be accomplished within moderate amount of time and little processing complexity. It is shown how OLAP techniques can help improve understandability and manageability of measurement data and, hence, improve the whole Performance Analysis process.
Abstract: This paper presents the work of signal discrimination
specifically for Electrocardiogram (ECG) waveform. ECG signal is
comprised of P, QRS, and T waves in each normal heart beat to
describe the pattern of heart rhythms corresponds to a specific
individual. Further medical diagnosis could be done to determine any
heart related disease using ECG information. The emphasis on QRS
Complex classification is further discussed to illustrate the
importance of it. Pan-Tompkins Algorithm, a widely known
technique has been adapted to realize the QRS Complex
classification process. There are eight steps involved namely
sampling, normalization, low pass filter, high pass filter (build a band
pass filter), derivation, squaring, averaging and lastly is the QRS
detection. The simulation results obtained is represented in a
Graphical User Interface (GUI) developed using MATLAB.
Abstract: The tracking allows to detect the tumor affections of cervical cancer, it is particularly complex and consuming time, because it consists in seeking some abnormal cells among a cluster of normal cells. In this paper, we present our proposed computer system for helping the doctors in tracking the cervical cancer. Knowing that the diagnosis of the malignancy is based in the set of atypical morphological details of all cells, herein, we present an unsupervised genetic algorithm for the separation of cell components since the diagnosis is doing by analysis of the core and the cytoplasm. We give also the various algorithms used for computing the morphological characteristics of cells (Ratio core/cytoplasm, cellular deformity, ...) necessary for the recognition of illness.
Abstract: Tumor classification is a key area of research in the
field of bioinformatics. Microarray technology is commonly used in
the study of disease diagnosis using gene expression levels. The
main drawback of gene expression data is that it contains thousands
of genes and a very few samples. Feature selection methods are used
to select the informative genes from the microarray. These methods
considerably improve the classification accuracy. In the proposed
method, Genetic Algorithm (GA) is used for effective feature
selection. Informative genes are identified based on the T-Statistics,
Signal-to-Noise Ratio (SNR) and F-Test values. The initial candidate
solutions of GA are obtained from top-m informative genes. The
classification accuracy of k-Nearest Neighbor (kNN) method is used
as the fitness function for GA. In this work, kNN and Support Vector
Machine (SVM) are used as the classifiers. The experimental results
show that the proposed work is suitable for effective feature
selection. With the help of the selected genes, GA-kNN method
achieves 100% accuracy in 4 datasets and GA-SVM method
achieves in 5 out of 10 datasets. The GA with kNN and SVM
methods are demonstrated to be an accurate method for microarray
based tumor classification.
Abstract: Volume rendering is widely used in medical CT image
visualization. Applying 3D image visualization to diagnosis
application can require accurate volume rendering with high
resolution. Interpolation is important in medical image processing
applications such as image compression or volume resampling.
However, it can distort the original image data because of edge
blurring or blocking effects when image enhancement procedures
were applied. In this paper, we proposed adaptive tension control
method exploiting gradient information to achieve high resolution
medical image enhancement in volume visualization, where restored
images are similar to original images as much as possible. The
experimental results show that the proposed method can improve
image quality associated with the adaptive tension control efficacy.
Abstract: The objective of this paper, is to apply support vector machine (SVM) approach for the classification of cancerous and normal regions of prostate images. Three kinds of textural features are extracted and used for the analysis: parameters of the Gauss- Markov random field (GMRF), correlation function and relative entropy. Prostate images are acquired by the system consisting of a microscope, video camera and a digitizing board. Cross-validated classification over a database of 46 images is implemented to evaluate the performance. In SVM classification, sensitivity and specificity of 96.2% and 97.0% are achieved for the 32x32 pixel block sized data, respectively, with an overall accuracy of 96.6%. Classification performance is compared with artificial neural network and k-nearest neighbor classifiers. Experimental results demonstrate that the SVM approach gives the best performance.
Abstract: In the micro and nano-technology industry, the
«clean-rooms» dedicated to manufacturing chip, are equipped with
the most sophisticated equipment-tools. There use a large number of
resources in according to strict specifications for an optimum
working and result. The distribution of «utilities» to the production is
assured by teams who use a supervision tool.
The studies show the interest to control the various parameters of
production or/and distribution, in real time, through a reliable and
effective supervision tool. This document looks at a large part of the
functions that the supervisor must assure, with complementary
functionalities to help the diagnosis and simulation that prove very
useful in our case where the supervised installations are complexed
and in constant evolution.
Abstract: The disaster from functional gastrointestinal disorders has detrimental impact on the quality of life of the effected population and imposes a tremendous social and economic burden. There are, however, rare diagnostic methods for the functional gastrointestinal disorders. Our research group identified recently that the gastrointestinal tract well in the patients with the functional gastrointestinal disorders becomes more rigid than healthy people when palpating the abdominal regions overlaying the gastrointestinal tract. Objective of current study is, therefore, identify feasibility of a diagnostic system for the functional gastrointestinal disorders based on ultrasound technique, which can quantify the characteristics above. Two-dimensional finite difference (FD) models (one normal and two rigid model) were developed to analyze the reflective characteristic (displacement) on each soft-tissue layer responded after application of ultrasound signals. The FD analysis was then based on elastic ultrasound theory. Validation of the model was performed via comparison of the characteristic of the ultrasonic responses predicted by FD analysis with that determined from the actual specimens for the normal and rigid conditions. Based on the results from FD analysis, ultrasound system for diagnosis of the functional gastrointestinal disorders was developed and clinically tested via application of it to 40 human subjects with/without functional gastrointestinal disorders who were assigned to Normal and Patient Groups. The FD models were favorably validated. The results from FD analysis showed that the maximum displacement amplitude in the rigid models (0.12 and 0.16) at the interface between the fat and muscle layers was explicitly less than that in the normal model (0.29). The results from actual specimens showed that the maximum amplitude of the ultrasonic reflective signal in the rigid models (0.2±0.1Vp-p) at the interface between the fat and muscle layers was explicitly higher than that in the normal model (0.1±0.2 Vp-p). Clinical tests using our customized ultrasound system showed that the maximum amplitudes of the ultrasonic reflective signals near to the gastrointestinal tract well for the patient group (2.6±0.3 Vp-p) were generally higher than those in normal group (0.1±0.2 Vp-p). Here, maximum reflective signals was appeared at 20mm depth approximately from abdominal skin for all human subjects, corresponding to the location of the boundary layer close to gastrointestinal tract well. These findings suggest that our customized ultrasound system using the ultrasonic reflective signal may be helpful to the diagnosis of the functional gastrointestinal disorders.
Abstract: The study of morphometric and histologic evolutions
of the Bursa of Fabricus during 27 weeks of post-hashing age,
realized on 88 subjects of broiler chicken they permitted to collect
information about the morpho-histological aspect according to their
post-hashing age; showed the size and the weight of the Bursa of
Fabricius which reach their maximum between the 10th and the 11th
week of age and the physiologic involution phenomena. These
variations are in close relationship to the sexual maturity. These
results can be used in the diagnosis of viral disease such as the
Gumboro disease, Marek disease.
Abstract: This paper presents a technique for diagnosis of the abdominal aorta aneurysm in magnetic resonance imaging (MRI) images. First, our technique is designed to segment the aorta image in MRI images. This is a required step to determine the volume of aorta image which is the important step for diagnosis of the abdominal aorta aneurysm. Our proposed technique can detect the volume of aorta in MRI images using a new external energy for snakes model. The new external energy for snakes model is calculated from Law-s texture. The new external energy can increase the capture range of snakes model efficiently more than the old external energy of snakes models. Second, our technique is designed to diagnose the abdominal aorta aneurysm by Bayesian classifier which is classification models based on statistical theory. The feature for data classification of abdominal aorta aneurysm was derived from the contour of aorta images which was a result from segmenting of our snakes model, i.e., area, perimeter and compactness. We also compare the proposed technique with the traditional snakes model. In our experiment results, 30 images are trained, 20 images are tested and compared with expert opinion. The experimental results show that our technique is able to provide more accurate results than 95%.
Abstract: Young patients suffering from Cerebral Palsy are
facing difficult choices concerning heavy surgeries. Diagnosis settled
by surgeons can be complex and on the other hand decision for
patient about getting or not such a surgery involves important
reflection effort. Proposed software combining prediction for
surgeries and post surgery kinematic values, and from 3D model
representing the patient is an innovative tool helpful for both patients
and medicine professionals. Beginning with analysis and
classification of kinematics values from Data Base extracted from
gait analysis in 3 separated clusters, it is possible to determine close
similarity between patients. Prediction surgery best adapted to
improve a patient gait is then determined by operating a suitable
preconditioned neural network. Finally, patient 3D modeling based
on kinematic values analysis, is animated thanks to post surgery
kinematic vectors characterizing the closest patient selected from
patients clustering.