Abstract: Image segmentation is the concept of splitting the images into several images. Image Segmentation algorithm is used to manipulate the process of image segmentation. The advantage of ABC is that it conducts every worldwide exploration and inhabitant exploration for iteration. Particle Swarm Optimization (PSO) and Evolutionary Particle Swarm Optimization (EPSO) encompass a number of search problems. Cleverness Artificial Bee Colony algorithm has been imposed to increase the performance of a neighborhood search. The simulation results clearly show that the presented ABC methods outperform the existing methods. The result shows that the algorithms can be used to implement the manipulator for grasping of colored objects. The efficiency of the presented method is improved a lot by comparing to other methods.
Abstract: In this study, it was aimed to determine a route for identification of rice cultivation areas within Thrace and Marmara regions of Turkey using remote sensing and GIS. Landsat 8 (OLI-TIRS) imageries acquired in production season of 2013 with 181/32 Path/Row number were used. Four different seasonal images were generated utilizing original bands and different transformation techniques. All images were classified individually using supervised classification techniques and Land Use Land Cover Maps (LULC) were generated with 8 classes. Areas (ha, %) of each classes were calculated. In addition, district-based rice distribution maps were developed and results of these maps were compared with Turkish Statistical Institute (TurkSTAT; TSI)’s actual rice cultivation area records. Accuracy assessments were conducted, and most accurate map was selected depending on accuracy assessment and coherency with TSI results. Additionally, rice areas on over 4° slope values were considered as mis-classified pixels and they eliminated using slope map and GIS tools. Finally, randomized rice zones were selected to obtain maximum-minimum value ranges of each date (May, June, July, August, September images separately) NDVI, LSWI, and LST images to test whether they may be used for rice area determination via raster calculator tool of ArcGIS. The most accurate classification for rice determination was obtained from seasonal LSWI LULC map, and considering TSI data and accuracy assessment results and mis-classified pixels were eliminated from this map. According to results, 83151.5 ha of rice areas exist within study area. However, this result is higher than TSI records with an area of 12702.3 ha. Use of maximum-minimum range of rice area NDVI, LSWI, and LST was tested in Meric district. It was seen that using the value ranges obtained from July imagery, gave the closest results to TSI records, and the difference was only 206.4 ha. This difference is normal due to relatively low resolution of images. Thus, employment of images with higher spectral, spatial, temporal and radiometric resolutions may provide more reliable results.
Abstract: Cinema constructs mind-spaces that reflect inherent human thoughts and emotions. As a representational art, Cinema would introduce comprehensive images of life phenomena in different ways. The term “represent” suggests verity of meanings; bring into presence, replace or typify. In that sense, Cinema may present a phenomenon through direct embodiment, or introduce a substitute image that replaces the original phenomena, or typify it by relating the produced image to a more general category through a process of abstraction. This research is interested in questioning the type of images that Egyptian Cinema introduces to informal urbanism and how these images were conditioned and reshaped in the last twenty years. The informalities/slums phenomenon first appeared in Egypt and, particularly, Cairo in the early sixties, however, this phenomenon was completely ignored by the state and society until the eighties, and furthermore, its evident representation in Cinema was by the mid-nineties. The Informal City represents the illegal housing developments, and it is a fast growing form of urbanization in Cairo. Yet, this expanding phenomenon is still depicted as the minority, exceptional and marginal through the Cinematic lenses. This paper aims at tracing the forms of representations of the urban informalities in the Egyptian Cinema between 1994 and 2014, and how did that affect the popular mind and its perception of these areas. The paper runs two main lines of inquiry; the first traces the phenomena through a chronological and geographical mapping of the informal urbanism has been portrayed in films. This analysis is based on an academic research work at Cairo University in Fall 2014. The visual tracing through maps and timelines allowed a reading of the phases of ignorance, presence, typifying and repetition in the representation of this huge sector of the city through more than 50 films that has been investigated. The analysis clearly revealed the “portrayed image” of informality by the Cinema through the examined period. However, the second part of the paper explores the “perceived image”. A designed questionnaire is applied to highlight the main features of that image that is perceived by both inhabitants of informalities and other Cairenes based on watching selected films. The questionnaire covers the different images of informalities proposed in the Cinema whether in a comic or a melodramatic background and highlight the descriptive terms used, to see which of them resonate with the mass perceptions and affected their mental images. The two images; “portrayed” and “perceived” are then to be encountered to reflect on issues of repetitions, stereotyping and reality. The formulated stereotype of informal urbanism is finally outlined and justified in relation to both production consumption mechanisms of films and the State official vision of informalities.
Abstract: Edge detection is one of the most important tasks in image processing. Medical image edge detection plays an important role in segmentation and object recognition of the human organs. It refers to the process of identifying and locating sharp discontinuities in medical images. In this paper, a neuro-fuzzy based approach is introduced to detect the edges for noisy medical images. This approach uses desired number of neuro-fuzzy subdetectors with a postprocessor for detecting the edges of medical images. The internal parameters of the approach are optimized by training pattern using artificial images. The performance of the approach is evaluated on different medical images and compared with popular edge detection algorithm. From the experimental results, it is clear that this approach has better performance than those of other competing edge detection algorithms for noisy medical images.
Abstract: Hyperspectral images and remote sensing are important for many applications. A problem in the use of these images is the high volume of data to be processed, stored and transferred. Dimensionality reduction techniques can be used to reduce the volume of data. In this paper, an approach to band selection based on clustering algorithms is presented. This approach allows to reduce the volume of data. The proposed structure is based on Fuzzy C-Means (or K-Means) and NWHFC algorithms. New attributes in relation to other studies in the literature, such as kurtosis and low correlation, are also considered. A comparison of the results of the approach using the Fuzzy C-Means and K-Means with different attributes is performed. The use of both algorithms show similar good results but, particularly when used attributes variance and kurtosis in the clustering process, however applicable in hyperspectral images.
Abstract: Recent developments on multi-agent system have brought a new research field on image processing. Several algorithms are used simultaneously and improved in deferent applications while new methods are investigated. This paper presents a new automatic method for edge detection using several agents and many different actions. The proposed multi-agent system is based on parallel agents that locally perceive their environment, that is to say, pixels and additional environmental information. This environment is built using Vector Field Convolution that attract free agent to the edges. Problems of partial, hidden or edges linking are solved with the cooperation between agents. The presented method was implemented and evaluated using several examples on different synthetic and medical images. The obtained experimental results suggest that this approach confirm the efficiency and accuracy of detected edge.
Abstract: Blood cell analysis plays a significant role in the diagnosis of human health. As an alternative to the traditional technique conducted by laboratory technicians, this paper presents an automatic white blood cell (leukocyte) detection system using Image Stitching and Color Overlapping Windows. The advantage of this method is to present a detection technique of white blood cells that are robust to imperfect shapes of blood cells with various image qualities. The input for this application is images from a microscope-slide translation video. The preprocessing stage is performed by stitching the input images. First, the overlapping parts of the images are determined, then stitching and blending processes of two input images are performed. Next, the Color Overlapping Windows is performed for white blood cell detection which consists of color filtering, window candidate checking, window marking, finds window overlaps, and window cropping processes. Experimental results show that this method could achieve an average of 82.12% detection accuracy of the leukocyte images.
Abstract: Although most digital cameras acquire images in a raw
format, based on a Color Filter Array that arranges RGB color
filters on a square grid of photosensors, most image compression
techniques do not use the raw data; instead, they use the rgb result
of an interpolation algorithm of the raw data. This approach is
inefficient and by performing a lossless compression of the raw data,
followed by pixel interpolation, digital cameras could be more power
efficient and provide images with increased resolution given that the
interpolation step could be shifted to an external processing unit. In
this paper, we conduct a survey on the use of lossless compression
algorithms with raw Bayer images. Moreover, in order to reduce the
effect of the transition between colors that increase the entropy of
the raw Bayer image, we split the image into three new images
corresponding to each channel (red, green and blue) and we study
the same compression algorithms applied to each one individually.
This simple pre-processing stage allows an improvement of more than
15% in predictive based methods.
Abstract: In this era of online communication, which transacts data in 0s and 1s, confidentiality is a priced commodity. Ensuring safe transmission of encrypted data and their uncorrupted recovery is a matter of prime concern. Among the several techniques for secure sharing of images, this paper proposes a k out of n region incrementing image sharing scheme for color images. The highlight of this scheme is the use of simple Boolean and arithmetic operations for generating shares and the Lagrange interpolation polynomial for authenticating shares. Additionally, this scheme addresses problems faced by existing algorithms such as color reversal and pixel expansion. This paper regenerates the original secret image whereas the existing systems regenerates only the half toned secret image.
Abstract: Effective statistical feature extraction and classification are important in image-based automatic inspection and analysis. An automatic wood species recognition system is designed to perform wood inspection at custom checkpoints to avoid mislabeling of timber which will results to loss of income to the timber industry. The system focuses on analyzing the statistical pores properties of the wood images. This paper proposed a fuzzy-based feature extractor which mimics the experts’ knowledge on wood texture to extract the properties of pores distribution from the wood surface texture. The proposed feature extractor consists of two steps namely pores extraction and fuzzy pores management. The total number of statistical features extracted from each wood image is 38 features. Then, a backpropagation neural network is used to classify the wood species based on the statistical features. A comprehensive set of experiments on a database composed of 5200 macroscopic images from 52 tropical wood species was used to evaluate the performance of the proposed feature extractor. The advantage of the proposed feature extraction technique is that it mimics the experts’ interpretation on wood texture which allows human involvement when analyzing the wood texture. Experimental results show the efficiency of the proposed method.
Abstract: To increase the temperature contrast in thermal
images, the characteristics of the electrical conductivity and thermal
imaging modalities can be combined. In this experimental study, it is
objected to observe whether the temperature contrast created by the
tumor tissue can be improved just due to the current application
within medical safety limits. Various thermal breast phantoms are
developed to simulate the female breast tissue. In vitro experiments
are implemented using a thermal infrared camera in a controlled
manner. Since experiments are implemented in vitro, there is no
metabolic heat generation and blood perfusion. Only the effects and
results of the electrical stimulation are investigated. Experimental
study is implemented with two-dimensional models. Temperature
contrasts due to the tumor tissues are obtained. Cancerous tissue is
determined using the difference and ratio of healthy and tumor
images. 1 cm diameter single tumor tissue causes almost 40 °mC
temperature contrast on the thermal-breast phantom. Electrode
artifacts are reduced by taking the difference and ratio of background
(healthy) and tumor images. Ratio of healthy and tumor images show
that temperature contrast is increased by the current application.
Abstract: Liver cancer is one of the common diseases that cause the death. Early detection is important to diagnose and reduce the incidence of death. Improvements in medical imaging and image processing techniques have significantly enhanced interpretation of medical images. Computer-Aided Diagnosis (CAD) systems based on these techniques play a vital role in the early detection of liver disease and hence reduce liver cancer death rate. This paper presents an automated CAD system consists of three stages; firstly, automatic liver segmentation and lesion’s detection. Secondly, extracting features. Finally, classifying liver lesions into benign and malignant by using the novel contrasting feature-difference approach. Several types of intensity, texture features are extracted from both; the lesion area and its surrounding normal liver tissue. The difference between the features of both areas is then used as the new lesion descriptors. Machine learning classifiers are then trained on the new descriptors to automatically classify liver lesions into benign or malignant. The experimental results show promising improvements. Moreover, the proposed approach can overcome the problems of varying ranges of intensity and textures between patients, demographics, and imaging devices and settings.
Abstract: Growth and remodeling of biological structures have
gained lots of attention over the past decades. Determining the
response of living tissues to mechanical loads is necessary for a wide
range of developing fields such as prosthetics design or computerassisted
surgical interventions. It is a well-known fact that biological
structures are never stress-free, even when externally unloaded. The
exact origin of these residual stresses is not clear, but theoretically,
growth is one of the main sources. Extracting body organ’s shapes
from medical imaging does not produce any information regarding
the existing residual stresses in that organ. The simplest cause of such
stresses is gravity since an organ grows under its influence from
birth. Ignoring such residual stresses might cause erroneous results in
numerical simulations. Accounting for residual stresses due to tissue
growth can improve the accuracy of mechanical analysis results. This
paper presents an original computational framework based on gradual
growth to determine the residual stresses due to growth. To illustrate
the method, we apply it to a finite element model of a healthy human
face reconstructed from medical images. The distribution of residual
stress in facial tissues is computed, which can overcome the effect of
gravity and maintain tissues firmness. Our assumption is that tissue
wrinkles caused by aging could be a consequence of decreasing
residual stress and thus not counteracting gravity. Taking into
account these stresses seems therefore extremely important in
maxillofacial surgery. It would indeed help surgeons to estimate
tissues changes after surgery.
Abstract: Magnetic Resonance Imaging Contrast Agents
(MRI-CM) are significant in the clinical and biological imaging as
they have the ability to alter the normal tissue contrast, thereby
affecting the signal intensity to enhance the visibility and detectability
of images. Superparamagnetic Iron Oxide (SPIO) nanoparticles,
coated with dextran or carboxydextran are currently available for
clinical MR imaging of the liver. Most SPIO contrast agents are
T2 shortening agents and Resovist (Ferucarbotran) is one of a
clinically tested, organ-specific, SPIO agent which has a low
molecular carboxydextran coating. The enhancement effect of
Resovist depends on its relaxivity which in turn depends on factors
like magnetic field strength, concentrations, nanoparticle properties,
pH and temperature. Therefore, this study was conducted to
investigate the impact of field strength and different contrast
concentrations on enhancement effects of Resovist. The study
explored the MRI signal intensity of Resovist in the physiological
range of plasma from T2-weighted spin echo sequence at three
magnetic field strengths: 0.47 T (r1=15, r2=101), 1.5 T (r1=7.4,
r2=95), and 3 T (r1=3.3, r2=160) and the range of contrast
concentrations by a mathematical simulation. Relaxivities of r1 and r2
(L mmol-1 Sec-1) were obtained from a previous study and the selected
concentrations were 0.05, 0.06, 0.07, 0.08, 0.09, 0.1, 0.2, 0.3, 0.4, 0.5,
0.6, 0.7, 0.8, 0.9, 1.0, 2.0, and 3.0 mmol/L. T2-weighted images were
simulated using TR/TE ratio as 2000 ms /100 ms. According to the
reference literature, with increasing magnetic field strengths, the
r1 relaxivity tends to decrease while the r2 did not show any
systematic relationship with the selected field strengths. In parallel,
this study results revealed that the signal intensity of Resovist at lower
concentrations tends to increase than the higher concentrations. The
highest reported signal intensity was observed in the low field strength
of 0.47 T. The maximum signal intensities for 0.47 T, 1.5 T and 3 T
were found at the concentration levels of 0.05, 0.06 and 0.05 mmol/L,
respectively. Furthermore, it was revealed that, the concentrations
higher than the above, the signal intensity was decreased
exponentially. An inverse relationship can be found between the field
strength and T2 relaxation time, whereas, the field strength was
increased, T2 relaxation time was decreased accordingly. However,
resulted T2 relaxation time was not significantly different between
0.47 T and 1.5 T in this study. Moreover, a linear correlation of
transverse relaxation rates (1/T2, s–1) with the concentrations of
Resovist can be observed. According to these results, it can conclude
that the concentration of SPIO nanoparticle contrast agents and the
field strengths of MRI are two important parameters which can affect the signal intensity of T2-weighted SE sequence. Therefore, when MR
imaging those two parameters should be considered prudently.
Abstract: This paper outlines the development of an
experimental technique in quantifying supersonic jet flows, in an
attempt to avoid seeding particle problems frequently associated with
particle-image velocimetry (PIV) techniques at high Mach numbers.
Based on optical flow algorithms, the idea behind the technique
involves using high speed cameras to capture Schlieren images of the
supersonic jet shear layers, before they are subjected to an adapted
optical flow algorithm based on the Horn-Schnuck method to
determine the associated flow fields. The proposed method is capable
of offering full-field unsteady flow information with potentially
higher accuracy and resolution than existing point-measurements or
PIV techniques. Preliminary study via numerical simulations of a
circular de Laval jet nozzle successfully reveals flow and shock
structures typically associated with supersonic jet flows, which serve
as useful data for subsequent validation of the optical flow based
experimental results. For experimental technique, a Z-type Schlieren
setup is proposed with supersonic jet operated in cold mode,
stagnation pressure of 4 bar and exit Mach of 1.5. High-speed singleframe
or double-frame cameras are used to capture successive
Schlieren images. As implementation of optical flow technique to
supersonic flows remains rare, the current focus revolves around
methodology validation through synthetic images. The results of
validation test offers valuable insight into how the optical flow
algorithm can be further improved to improve robustness and
accuracy. Despite these challenges however, this supersonic flow
measurement technique may potentially offer a simpler way to
identify and quantify the fine spatial structures within the shock shear
layer.
Abstract: In order to retrieve images efficiently from a large
database, a unique method integrating color and texture features
using genetic programming has been proposed. Opponent color
histogram which gives shadow, shade, and light intensity invariant
property is employed in the proposed framework for extracting color
features. For texture feature extraction, fast discrete curvelet
transform which captures more orientation information at different
scales is incorporated to represent curved like edges. The recent
scenario in the issues of image retrieval is to reduce the semantic gap
between user’s preference and low level features. To address this
concern, genetic algorithm combined with relevance feedback is
embedded to reduce semantic gap and retrieve user’s preference
images. Extensive and comparative experiments have been conducted
to evaluate proposed framework for content based image retrieval on
two databases, i.e., COIL-100 and Corel-1000. Experimental results
clearly show that the proposed system surpassed other existing
systems in terms of precision and recall. The proposed work achieves
highest performance with average precision of 88.2% on COIL-100
and 76.3% on Corel, the average recall of 69.9% on COIL and 76.3%
on Corel. Thus, the experimental results confirm that the proposed
content based image retrieval system architecture attains better
solution for image retrieval.
Abstract: Liver segmentation from medical images poses more
challenges than analogous segmentations of other organs. This
contribution introduces a liver segmentation method from a series of
computer tomography images. Overall, we present a novel method for
segmenting liver by coupling density matching with shape priors.
Density matching signifies a tracking method which operates via
maximizing the Bhattacharyya similarity measure between the
photometric distribution from an estimated image region and a model
photometric distribution. Density matching controls the direction of
the evolution process and slows down the evolving contour in regions
with weak edges. The shape prior improves the robustness of density
matching and discourages the evolving contour from exceeding liver’s
boundaries at regions with weak boundaries. The model is
implemented using a modified distance regularized level set (DRLS)
model. The experimental results show that the method achieves a
satisfactory result. By comparing with the original DRLS model, it is
evident that the proposed model herein is more effective in addressing
the over segmentation problem. Finally, we gauge our performance of
our model against matrices comprising of accuracy, sensitivity, and
specificity.
Abstract: Magnetic Resonance Imaging (MRI) is one of the
most important medical imaging modality. Subjective assessment of
the image quality is regarded as the gold standard to evaluate MR
images. In this study, a database of 210 MR images which contains
ten reference images and 200 distorted images is presented. The
reference images were distorted with four types of distortions: Rician
Noise, Gaussian White Noise, Gaussian Blur and DCT compression.
The 210 images were assessed by ten subjects. The subjective scores
were presented in Difference Mean Opinion Score (DMOS). The
DMOS values were compared with four FR-IQA metrics. We have
used Pearson Linear Coefficient (PLCC) and Spearman Rank Order
Correlation Coefficient (SROCC) to validate the DMOS values. The
high correlation values of PLCC and SROCC shows that the DMOS
values are close to the objective FR-IQA metrics.
Abstract: In this glasshouse study, we developed a new imagebased
non-destructive technique for detecting leaf P status of
different crops such as cotton, tomato and lettuce. The plants were
grown on a nutrient solution containing different P concentrations,
e.g. 0%, 50% and 100% of recommended P concentration (P0 = no P,
L; P1 = 2.5 mL 10 L-1 of P and P2 = 5 mL 10 L-1 of P). After 7 weeks
of treatment, the plants were harvested and data on leaf P contents
were collected using the standard destructive laboratory method and
at the same time leaf images were collected by a handheld crop image
sensor. We calculated leaf area, leaf perimeter and RGB (red, green
and blue) values of these images. These data were further used in
linear discriminant analysis (LDA) to estimate leaf P contents, which
successfully classified these plants on the basis of leaf P contents.
The data indicated that P deficiency in crop plants can be predicted
using leaf image and morphological data. Our proposed nondestructive
imaging method is precise in estimating P requirements of
different crop species.
Abstract: Cancer is still one of the serious diseases threatening
the lives of human beings. How to have an early diagnosis and
effective treatment for tumors is a very important issue. The animal
carcinoma model can provide a simulation tool for the studies of
pathogenesis, biological characteristics, and therapeutic effects.
Recently, drug delivery systems have been rapidly developed to
effectively improve the therapeutic effects. Liposome plays an
increasingly important role in clinical diagnosis and therapy for
delivering a pharmaceutic or contrast agent to the targeted sites.
Liposome can be absorbed and excreted by the human body, and is
well known that no harm to the human body. This study aimed to
compare the therapeutic effects between encapsulated (doxorubicin
liposomal, Lipodox) and un-encapsulated (doxorubicin, Dox)
anti-tumor drugs using magnetic resonance imaging (MRI).
Twenty-four New Zealand rabbits implanted with VX2 carcinoma at
left thighs were classified into three groups: control group (untreated),
Dox-treated group, and LipoDox-treated group, 8 rabbits for each
group. MRI scans were performed three days after tumor implantation.
A 1.5T GE Signa HDxt whole body MRI scanner with a high
resolution knee coil was used in this study. After a 3-plane localizer
scan was performed, three-dimensional (3D) fast spin echo (FSE)
T2-weighted Images (T2WI) was used for tumor volumetric
quantification. Afterwards, two-dimensional (2D) spoiled gradient
recalled echo (SPGR) dynamic contrast-enhanced (DCE) MRI was
used for tumor perfusion evaluation. DCE-MRI was designed to
acquire four baseline images, followed by contrast agent Gd-DOTA
injection through the ear vein of rabbit. A series of 32 images were
acquired to observe the signals change over time in the tumor and
muscle. The MRI scanning was scheduled on a weekly basis for a
period of four weeks to observe the tumor progression longitudinally.
The Dox and LipoDox treatments were prescribed 3 times in the first
week immediately after the first MRI scan; i.e. 3 days after VX2 tumor
implantation. ImageJ was used to quantitate tumor volume and time
course signal enhancement on DCE images. The changes of tumor size
showed that the growth of VX2 tumors was effectively inhibited for
both LipoDox-treated and Dox-treated groups. Furthermore, the tumor
volume of LipoDox-treated group was significantly lower than that of
Dox-treated group, which implies that LipoDox has better therapeutic effect than Dox. The signal intensity of LipoDox-treated group is
significantly lower than that of the other two groups, which implies
that targeted therapeutic drug remained in the tumor tissue. This study
provides a radiation-free and non-invasive MRI method for
therapeutic monitoring of targeted liposome on an animal tumor
model.