Abstract: One of the most important challenging factors in
medical images is nominated as noise. Image denoising refers to the
improvement of a digital medical image that has been infected by
Additive White Gaussian Noise (AWGN). The digital medical image
or video can be affected by different types of noises. They are
impulse noise, Poisson noise and AWGN. Computed tomography
(CT) images are subjects to low quality due to the noise. Quality of
CT images is dependent on absorbed dose to patients directly in such
a way that increase in absorbed radiation, consequently absorbed
dose to patients (ADP), enhances the CT images quality. In this
manner, noise reduction techniques on purpose of images quality
enhancement exposing no excess radiation to patients is one the
challenging problems for CT images processing. In this work, noise
reduction in CT images was performed using two different
directional 2 dimensional (2D) transformations; i.e., Curvelet and
Contourlet and Discrete Wavelet Transform (DWT) thresholding
methods of BayesShrink and AdaptShrink, compared to each other
and we proposed a new threshold in wavelet domain for not only
noise reduction but also edge retaining, consequently the proposed
method retains the modified coefficients significantly that result good
visual quality. Data evaluations were accomplished by using two
criterions; namely, peak signal to noise ratio (PSNR) and Structure
similarity (Ssim).
Abstract: In some applications, such as image recognition or
compression, segmentation refers to the process of partitioning a
digital image into multiple segments. Image segmentation is typically
used to locate objects and boundaries (lines, curves, etc.) in images.
Image segmentation is to classify or cluster an image into several
parts (regions) according to the feature of image, for example, the
pixel value or the frequency response. More precisely, image
segmentation is the process of assigning a label to every pixel in an
image such that pixels with the same label share certain visual
characteristics. The result of image segmentation is a set of segments
that collectively cover the entire image, or a set of contours extracted
from the image. Several image segmentation algorithms were
proposed to segment an image before recognition or compression. Up
to now, many image segmentation algorithms exist and be
extensively applied in science and daily life. According to their
segmentation method, we can approximately categorize them into
region-based segmentation, data clustering, and edge-base
segmentation. In this paper, we give a study of several popular image
segmentation algorithms that are available.
Abstract: Tamil handwritten document is taken as a key source of data to identify the writer. Tamil is a classical language which has 247 characters include compound characters, consonants, vowels and special character. Most characters of Tamil are multifaceted in nature. Handwriting is a unique feature of an individual. Writer may change their handwritings according to their frame of mind and this place a risky challenge in identifying the writer. A new discriminative model with pooled features of handwriting is proposed and implemented using support vector machine. It has been reported on 100% of prediction accuracy by RBF and polynomial kernel based classification model.
Abstract: This paper treats different aspects of entropy measure
in classical information theory and statistical quantum mechanics, it
presents the possibility of extending the definition of Von Neumann
entropy to image and array processing. In the first part, we generalize
the quantum entropy using singular values of arbitrary rectangular
matrices to measure the randomness and the quality of denoising
operation, this new definition of entropy can be implemented to
compare the performance analysis of filtering methods. In the second
part, we apply the concept of pure state in quantum formalism
to generalize the maximum entropy method for narrowband and
farfield source localization problem. Several computer simulation
results are illustrated to demonstrate the effectiveness of the proposed
techniques.
Abstract: Computer aided diagnosis systems provide vital
opinion to radiologists in the detection of early signs of breast cancer
from mammogram images. Architectural distortions, masses and
microcalcifications are the major abnormalities. In this paper, a
computer aided diagnosis system has been proposed for
distinguishing abnormal mammograms with architectural distortion
from normal mammogram. Four types of texture features GLCM
texture, GLRLM texture, fractal texture and spectral texture features
for the regions of suspicion are extracted. Support vector machine
has been used as classifier in this study. The proposed system yielded
an overall sensitivity of 96.47% and an accuracy of 96% for
mammogram images collected from digital database for screening
mammography database.
Abstract: In this paper, we present a new segmentation approach
for focal liver lesions in contrast enhanced ultrasound imaging. This
approach, based on a two-cluster Fuzzy C-Means methodology,
considers type-II fuzzy sets to handle uncertainty due to the image
modality (presence of speckle noise, low contrast, etc.), and to
calculate the optimum inter-cluster threshold. Fine boundaries are
detected by a local recursive merging of ambiguous pixels. The
method has been tested on a representative database. Compared to
both Otsu and type-I Fuzzy C-Means techniques, the proposed
method significantly reduces the segmentation errors.
Abstract: One of the most critical decision points in the design of a
face recognition system is the choice of an appropriate face representation.
Effective feature descriptors are expected to convey sufficient, invariant
and non-redundant facial information. In this work we propose a set of
Hahn moments as a new approach for feature description. Hahn moments
have been widely used in image analysis due to their invariance, nonredundancy
and the ability to extract features either globally and locally.
To assess the applicability of Hahn moments to Face Recognition we
conduct two experiments on the Olivetti Research Laboratory (ORL)
database and University of Notre-Dame (UND) X1 biometric collection.
Fusion of the global features along with the features from local facial
regions are used as an input for the conventional k-NN classifier. The
method reaches an accuracy of 93% of correctly recognized subjects for
the ORL database and 94% for the UND database.
Abstract: Indonesia has experienced annual forest fires that have
rapidly destroyed and degraded its forests. Fires in the peat swamp
forests of Riau Province, have set the stage for problems to worsen,
this being the ecosystem most prone to fires (which are also the most
difficult, to extinguish). Despite various efforts to curb deforestation,
and forest degradation processes, severe forest fires are still
occurring. To find an effective solution, the basic causes of the
problems must be identified. It is therefore critical to have an indepth
understanding of the underlying causal factors that have
contributed to deforestation and forest degradation as a whole, in
order to attain reductions in their rates. An assessment of the drivers of deforestation and forest
degradation was carried out, in order to design and implement
measures that could slow these destructive processes. Research was
conducted in Giam Siak Kecil–Bukit Batu Biosphere Reserve
(GSKBB BR), in the Riau Province of Sumatera, Indonesia. A
biosphere reserve was selected as the study site because such reserves
aim to reconcile conservation with sustainable development. A
biosphere reserve should promote a range of local human activities,
together with development values that are in line spatially and
economically with the area conservation values, through use of a
zoning system. Moreover, GSKBB BR is an area with vast peatlands,
and is experiencing forest fires annually. Various factors were
analysed to assess the drivers of deforestation and forest degradation
in GSKBB BR; data were collected from focus group discussions
with stakeholders, key informant interviews with key stakeholders,
field observation and a literature review. Landsat satellite imagery was used to map forest-cover changes
for various periods. Analysis of landsat images, taken during the
period 2010-2014, revealed that within the non-protected area of core
zone, there was a trend towards decreasing peat swamp forest areas,
increasing land clearance, and increasing areas of community oilpalm
and rubber plantations. Fire was used for land clearing and most
of the forest fires occurred in the most populous area (the transition
area). The study found a relationship between the deforested/
degraded areas, and certain distance variables, i.e. distance from
roads, villages and the borders between the core area and the buffer
zone. The further the distance from the core area of the reserve, the
higher was the degree of deforestation and forest degradation. Research findings suggested that agricultural expansion may be
the direct cause of deforestation and forest degradation in the reserve,
whereas socio-economic factors were the underlying driver of forest
cover changes; such factors consisting of a combination of sociocultural,
infrastructural, technological, institutional (policy and governance), demographic (population pressure) and economic
(market demand) considerations. These findings indicated that local
factors/problems were the critical causes of deforestation and
degradation in GSKBB BR. This research therefore concluded that
reductions in deforestation and forest degradation in GSKBB BR
could be achieved through ‘local actor’-tailored approaches such as
community empowerment.
Abstract: This report presents an alternative technique of
application of contrast agent in vivo, i.e. before sampling. By this
new method the electron micrograph of tissue sections have an
acceptable contrast compared to other methods and present no artifact
of precipitation on sections. Another advantage is that a small amount
of contrast is needed to get a good result given that most of them are
expensive and extremely toxic.
Abstract: The purpose of this study is the discrimination of 28
postmenopausal with osteoporotic femoral fractures from an agematched
control group of 28 women using texture analysis based on
fractals. Two pre-processing approaches are applied on radiographic
images; these techniques are compared to highlight the choice of the
pre-processing method. Furthermore, the values of the fractal
dimension are compared to those of the fractal signature in terms of
the classification of the two populations. In a second analysis, the
BMD measure at proximal femur was compared to the fractal
analysis, the latter, which is a non-invasive technique, allowed a
better discrimination; the results confirm that the fractal analysis of
texture on calcaneus radiographs is able to discriminate osteoporotic
patients with femoral fracture from controls. This discrimination was
efficient compared to that obtained by BMD alone. It was also
present in comparing subgroups with overlapping values of BMD.
Abstract: The use of titanium fluoride and iron fluoride
(TiF3/FeF3) catalysts in combination with polutetrafluoroethylene
(PTFE) in plain zinc- dialkyldithiophosphate (ZDDP) oil is important
for the study of engine tribocomponents and is increasingly a strategy
to improve the formation of tribofilm and provide low friction and
excellent wear protection in reduced phosphorus plain ZDDP oil. The
influence of surface roughness and the concentration of
TiF3/FeF3/PTFE were investigated using bearing steel samples
dipped in lubricant solution at 100°C for two different heating time
durations. This paper addresses the effects of water drop contact
angle using different surface; finishes after treating them with
different lubricant combination. The calculated water drop contact
angles were analyzed using Design of Experiment software (DOE)
and it was determined that a 0.05 μm Ra surface roughness would
provide an excellent TiF3/FeF3/PTFE coating for antiwear resistance
as reflected in the Scanning electron microscopy (SEM) images and
the tribological testing under extreme pressure conditions. Both
friction and wear performance depend greatly on the PTFE/and
catalysts in plain ZDDP oil with 0.05 % phosphorous and on the
surface finish of bearing steel. The friction and wear reducing effects,
which was observed in the tribological tests, indicated a better micro
lubrication effect of the 0.05 μm Ra surface roughness treated at
100°C for 24 hours when compared to the 0.1 μm Ra surface
roughness with the same treatment.
Abstract: The purpose of this project is to propose a quick and
environmentally friendly alternative to measure the quality of oils
used in food industry. There is evidence that repeated and
indiscriminate use of oils in food processing cause physicochemical
changes with formation of potentially toxic compounds that can
affect the health of consumers and cause organoleptic changes. In
order to assess the quality of oils, non-destructive optical techniques
such as Interferometry offer a rapid alternative to the use of reagents,
using only the interaction of light on the oil. Through this project, we
used interferograms of samples of oil placed under different heating
conditions to establish the changes in their quality. These
interferograms were obtained by means of a Mach-Zehnder
Interferometer using a beam of light from a HeNe laser of 10mW at
632.8nm. Each interferogram was captured, analyzed and measured
full width at half-maximum (FWHM) using the software from
Amcap and ImageJ. The total of FWHMs was organized in three
groups. It was observed that the average obtained from each of the
FWHMs of group A shows a behavior that is almost linear, therefore
it is probable that the exposure time is not relevant when the oil is
kept under constant temperature. Group B exhibits a slight
exponential model when temperature raises between 373 K and 393
K. Results of the t-Student show a probability of 95% (0.05) of the
existence of variation in the molecular composition of both samples.
Furthermore, we found a correlation between the Iodine Indexes
(Physicochemical Analysis) and the Interferograms (Optical
Analysis) of group C. Based on these results, this project highlights
the importance of the quality of the oils used in food industry and
shows how Interferometry can be a useful tool for this purpose.
Abstract: This paper presents the local mesh co-occurrence
patterns (LMCoP) using HSV color space for image retrieval system.
HSV color space is used in this method to utilize color, intensity and
brightness of images. Local mesh patterns are applied to define the
local information of image and gray level co-occurrence is used to
obtain the co-occurrence of LMeP pixels. Local mesh co-occurrence
pattern extracts the local directional information from local mesh
pattern and converts it into a well-mannered feature vector using gray
level co-occurrence matrix. The proposed method is tested on three
different databases called MIT VisTex, Corel, and STex. Also, this
algorithm is compared with existing methods, and results in terms of
precision and recall are shown in this paper.
Abstract: In this paper, we present a new segmentation approach
for liver lesions in regions of interest within MRI (Magnetic
Resonance Imaging). This approach, based on a two-cluster Fuzzy CMeans
methodology, considers the parameter variable compactness
to handle uncertainty. Fine boundaries are detected by a local
recursive merging of ambiguous pixels with a sequential forward
floating selection with Zernike moments. The method has been tested
on both synthetic and real images. When applied on synthetic images,
the proposed approach provides good performance, segmentations
obtained are accurate, their shape is consistent with the ground truth,
and the extracted information is reliable. The results obtained on MR
images confirm such observations. Our approach allows, even for
difficult cases of MR images, to extract a segmentation with good
performance in terms of accuracy and shape, which implies that the
geometry of the tumor is preserved for further clinical activities (such
as automatic extraction of pharmaco-kinetics properties, lesion
characterization, etc.).
Abstract: Artificial neural networks have gained a lot of interest
as empirical models for their powerful representational capacity,
multi input and output mapping characteristics. In fact, most feedforward
networks with nonlinear nodal functions have been proved to
be universal approximates. In this paper, we propose a new
supervised method for color image classification based on selforganizing
feature maps (SOFM). This algorithm is based on
competitive learning. The method partitions the input space using
self-organizing feature maps to introduce the concept of local
neighborhoods. Our image classification system entered into RGB
image. Experiments with simulated data showed that separability of
classes increased when increasing training time. In additional, the
result shows proposed algorithms are effective for color image
classification.
Abstract: Aurèsregion is one of the arid and semi-arid areas that
have suffered climate crises and overexploitation of natural resources
they have led to significant land degradation. The use of remote sensing data allowed us to analyze the land and
its spatiotemporal changes in the Aurès between 1987 and 2013, for
this work, we adopted a method of analysis based on the exploitation
of the images satellite Landsat TM 1987 and Landsat OLI 2013, from
the supervised classification likelihood coupled with field surveys of
the mission of May and September of 2013. Using ENVI EX software by the superposition of the ground cover
maps from 1987 and 2013, one can extract a spatial map change of
different land cover units. The results show that between 1987 and
2013 vegetation has suffered negative changes are the significant
degradation of forests and steppe rangelands, and sandy soils and
bare land recorded a considerable increase. The spatial change map land cover units between 1987 and 2013
allows us to understand the extensive or regressive orientation of
vegetation and soil, this map shows that dense forests give his place
to clear forests and steppe vegetation develops from a degraded forest
vegetation and bare, sandy soils earn big steppe surfaces that explain
its remarkable extension.
The analysis of remote sensing data highlights the profound
changes in our environment over time and quantitative monitoring of
the risk of desertification.
Abstract: Optic disk segmentation plays a key role in the mass
screening of individuals with diabetic retinopathy and glaucoma
ailments. An efficient hardware-based algorithm for optic disk
localization and segmentation would aid for developing an automated
retinal image analysis system for real time applications. Herein,
TMS320C6416DSK DSP board pixel intensity based fractal analysis
algorithm for an automatic localization and segmentation of the optic
disk is reported. The experiment has been performed on color and
fluorescent angiography retinal fundus images. Initially, the images
were pre-processed to reduce the noise and enhance the quality. The
retinal vascular tree of the image was then extracted using canny
edge detection technique. Finally, a pixel intensity based fractal
analysis is performed to segment the optic disk by tracing the origin
of the vascular tree. The proposed method is examined on three
publicly available data sets of the retinal image and also with the data
set obtained from an eye clinic. The average accuracy achieved is
96.2%. To the best of the knowledge, this is the first work reporting
the use of TMS320C6416DSK DSP board and pixel intensity based
fractal analysis algorithm for an automatic localization and
segmentation of the optic disk. This will pave the way for developing
devices for detection of retinal diseases in the future.
Abstract: In this paper, we present a four-step ortho-rectification
procedure for real-time geo-referencing of video data from a low-cost
UAV equipped with a multi-sensor system. The basic procedures for
the real-time ortho-rectification are: (1) decompilation of the video
stream into individual frames; (2) establishing the interior camera
orientation parameters; (3) determining the relative orientation
parameters for each video frame with respect to each other; (4)
finding the absolute orientation parameters, using a self-calibration
bundle and adjustment with the aid of a mathematical model. Each
ortho-rectified video frame is then mosaicked together to produce a
mosaic image of the test area, which is then merged with a well
referenced existing digital map for the purpose of geo-referencing
and aerial surveillance. A test field located in Abuja, Nigeria was
used to evaluate our method. Video and telemetry data were collected
for about fifteen minutes, and they were processed using the four-step
ortho-rectification procedure. The results demonstrated that the
geometric measurement of the control field from ortho-images is
more accurate when compared with those from original perspective
images when used to pin point the exact location of targets on the
video imagery acquired by the UAV. The 2-D planimetric accuracy
when compared with the 6 control points measured by a GPS receiver
is between 3 to 5 metres.
Abstract: Studying stress and strain trends in the femur and
recognizing femur failure mechanism is very important for
preventing hip fracture in the elderly. The aim of this study was to
identify high stress and strain regions in the femur during normal
walking and falling to find the mechanical behavior and failure
mechanism of the femur. We developed a finite element model of the
femur from the subject’s quantitative computed tomography (QCT)
image and used it to identify potentially high stress and strain regions
during the single-leg stance and the sideways fall. It was found that
fracture may initiate from the superior region of femoral neck and
propagate to the inferior region during a high impact force such as
sideways fall. The results of this study showed that the femur bone is
more sensitive to strain than stress which indicates the effect of
strain, in addition to effect of stress, should be considered for failure
analysis.
Abstract: Background subtraction and temporal difference are
often used for moving object detection in video. Both approaches are
computationally simple and easy to be deployed in real-time image
processing. However, while the background subtraction is highly
sensitive to dynamic background and illumination changes, the
temporal difference approach is poor at extracting relevant pixels of
the moving object and at detecting the stopped or slowly moving
objects in the scene. In this paper, we propose a simple moving object
detection scheme based on adaptive background subtraction and
temporal difference exploiting dynamic background updates. The
proposed technique consists of histogram equalization, a linear
combination of background and temporal difference, followed by the
novel frame-based and pixel-based background updating techniques.
Finally, morphological operations are applied to the output images.
Experimental results show that the proposed algorithm can solve the
drawbacks of both background subtraction and temporal difference
methods and can provide better performance than that of each method.