Abstract: Image segmentation has a fundamental role in analysis and interpretation for many applications. The automated segmentation of organs and tissues throughout the body using computed imaging has been rapidly increasing. Indeed, it represents one of the most important parts of clinical diagnostic tools. In this paper, we discuss a thorough literature review of recent methods of tumour segmentation from medical images which are briefly explained with the recent contribution of various researchers. This study was followed by comparing these methods in order to define new directions to develop and improve the performance of the segmentation of the tumour area from medical images.
Abstract: Piezoelectric linear motors have the characteristics of great electromagnetic compatibility, high positioning accuracy, compact structure and no deceleration mechanism, which make it promising to applicate in micro-miniature precision drive systems. However, most piezoelectric motors are employed by flexible clamping, which has insufficient rigidity and is difficult to use in rapid positioning. Another problem is that this clamping method seriously affects the vibration efficiency of the vibrating unit. In order to solve these problems, this paper proposes a piezoelectric stack linear motor based on double-end rigid clamping. First, a piezoelectric linear motor with a length of only 35.5 mm is designed. This motor is mainly composed of a motor stator, a driving foot, a ceramic friction strip, a linear guide, a pre-tightening mechanism and a base. This structure is much simpler and smaller than most similar motors, and it is easy to assemble as well as to realize precise control. In addition, the properties of piezoelectric stack are reviewed and in order to obtain the elliptic motion trajectory of the driving head, a driving scheme of the longitudinal-shear composite stack is innovatively proposed. Finally, impedance analysis and speed performance testing were performed on the piezoelectric linear motor prototype. The motor can measure speed up to 25.5 mm/s under the excitation of signal voltage of 120 V and frequency of 390 Hz. The result shows that the proposed piezoelectric stacked linear motor obtains great performance. It can run smoothly in a large speed range, which is suitable for various precision control in medical images, aerospace, precision machinery and many other fields.
Abstract: Telemedicine services use a large amount of data, most of which are diagnostic images in Digital Imaging and Communications in Medicine (DICOM) and Health Level Seven (HL7) formats. Metadata is generated from each related image to support their identification. This study presents the use of decision trees for the optimization of information search processes for diagnostic images, hosted on the cloud server. To analyze the performance in the server, the following quality of service (QoS) metrics are evaluated: delay, bandwidth, jitter, latency and throughput in five test scenarios for a total of 26 experiments during the loading and downloading of DICOM images, hosted by the telemedicine group server of the Universidad Militar Nueva Granada, Bogotá, Colombia. By applying decision trees as a data mining technique and comparing it with the sequential search, it was possible to evaluate the search times of diagnostic images in the server. The results show that by using the metadata in decision trees, the search times are substantially improved, the computational resources are optimized and the request management of the telemedicine image service is improved. Based on the experiments carried out, search efficiency increased by 45% in relation to the sequential search, given that, when downloading a diagnostic image, false positives are avoided in management and acquisition processes of said information. It is concluded that, for the diagnostic images services in telemedicine, the technique of decision trees guarantees the accessibility and robustness in the acquisition and manipulation of medical images, in improvement of the diagnoses and medical procedures in patients.
Abstract: In telemedicine, the image repository service is important to increase the accuracy of diagnostic support of medical personnel. This study makes comparison between two routing algorithms regarding the quality of service (QoS), to be able to analyze the optimal performance at the time of loading and/or downloading of medical images. This study focused on comparing the performance of Tabu Search with other heuristic and metaheuristic algorithms that improve QoS in telemedicine services in Colombia. For this, Tabu Search and Simulated Annealing heuristic algorithms are chosen for their high usability in this type of applications; the QoS is measured taking into account the following metrics: Delay, Throughput, Jitter and Latency. In addition, routing tests were carried out on ten images in digital image and communication in medicine (DICOM) format of 40 MB. These tests were carried out for ten minutes with different traffic conditions, reaching a total of 25 tests, from a server of Universidad Militar Nueva Granada (UMNG) in Bogotá-Colombia to a remote user in Universidad de Santiago de Chile (USACH) - Chile. The results show that Tabu search presents a better QoS performance compared to Simulated Annealing, managing to optimize the routing of medical images, a basic requirement to offer diagnostic images services in telemedicine.
Abstract: In the course of recent decades, medical imaging has
been dominated by the use of costly film media for review and
archival of medical investigation, however due to developments in
networks technologies and common acceptance of a standard digital
imaging and communication in medicine (DICOM) another approach
in light of World Wide Web was produced. Web technologies
successfully used in telemedicine applications, the combination of
web technologies together with DICOM used to design a web-based
and open source DICOM viewer. The Web server allowance to
inquiry and recovery of images and the images viewed/manipulated
inside a Web browser without need for any preinstalling software.
The dynamic site page for medical images visualization and
processing created by using JavaScript and HTML5 advancements.
The XAMPP ‘apache server’ is used to create a local web server for
testing and deployment of the dynamic site. The web-based viewer
connected to multiples devices through local area network (LAN) to
distribute the images inside healthcare facilities. The system offers a
few focal points over ordinary picture archiving and communication
systems (PACS): easy to introduce, maintain and independently
platforms that allow images to display and manipulated efficiently,
the system also user-friendly and easy to integrate with an existing
system that have already been making use of web technologies. The
wavelet-based image compression technique on which 2-D discrete
wavelet transform used to decompose the image then wavelet
coefficients are transmitted by entropy encoding after threshold to
decrease transmission time, stockpiling cost and capacity. The
performance of compression was estimated by using images quality
metrics such as mean square error ‘MSE’, peak signal to noise ratio
‘PSNR’ and compression ratio ‘CR’ that achieved (83.86%) when
‘coif3’ wavelet filter is used.
Abstract: This paper discusses the current trends in medical
image registration techniques and addresses the need to provide a
solid theoretical foundation for research endeavours. Methodological
analysis and synthesis of quality literature was done, providing a
platform for developing a good foundation for research study in
this field which is crucial in understanding the existing levels of
knowledge. Research on medical image registration techniques assists
clinical and medical practitioners in diagnosis of tumours and lesion
in anatomical organs, thereby enhancing fast and accurate curative
treatment of patients. Literature review aims to provide a solid
theoretical foundation for research endeavours in image registration
techniques. Developing a solid foundation for a research study is
possible through a methodological analysis and synthesis of existing
contributions. Out of these considerations, the aim of this paper is
to enhance the scientific community’s understanding of the current
status of research in medical image registration techniques and also
communicate to them, the contribution of this research in the field of
image processing. The gaps identified in current techniques can be
closed by use of artificial neural networks that form learning systems
designed to minimise error function. The paper also suggests several
areas of future research in the image registration.
Abstract: Data hiding can be achieved by Steganography or invisible digital watermarking. For digital watermarking, both accurate retrieval of the embedded watermark and the integrity of the cover image are important. Medical image security in Teleradiology is one of the applications where the embedded patient record needs to be extracted with accuracy as well as the medical image integrity verified. In this research paper, the Constant Correlation Spread Spectrum digital watermarking for medical image tamper detection and accurate embedded watermark retrieval is introduced. In the proposed method, a watermark bit from a patient record is spread in a medical image sub-block such that the correlation of all watermarked sub-blocks with a spreading code, W, would have a constant value, p. The constant correlation p, spreading code, W and the size of the sub-blocks constitute the secret key. Tamper detection is achieved by flagging any sub-block whose correlation value deviates by more than a small value, ℇ, from p. The major features of our new scheme include: (1) Improving watermark detection accuracy for high-pixel depth medical images by reducing the Bit Error Rate (BER) to Zero and (2) block-level tamper detection in a single computational process with simultaneous watermark detection, thereby increasing utility with the same computational cost.
Abstract: Medical images are important to help identifying different diseases, for example, Magnetic resonance imaging (MRI) can be used to investigate the brain, spinal cord, bones, joints, breasts, blood vessels, and heart. Image segmentation, in medical image analysis, is usually the first step to find out some characteristics with similar color, intensity or texture so that the diagnosis could be further carried out based on these features. This paper introduces an improved C-means model to segment the MRI images. The model is based on information entropy to evaluate the segmentation results by achieving global optimization. Several contributions are significant. Firstly, Genetic Algorithm (GA) is used for achieving global optimization in this model where fuzzy C-means clustering algorithm (FCMA) is not capable of doing that. Secondly, the information entropy after segmentation is used for measuring the effectiveness of MRI image processing. Experimental results show the outperformance of the proposed model by comparing with traditional approaches.
Abstract: The motivation of image compression technique is to reduce the irrelevance and redundancy of the image data in order to store or pass data in an efficient way from one place to another place. There are several types of compression methods available. Without the help of compression technique, the file size is knowingly larger, usually several megabytes, but by doing the compression technique, it is possible to reduce file size up to 10% as of the original without noticeable loss in quality. Image compression can be lossless or lossy. The compression technique can be applied to images, audio, video and text data. This research work mainly concentrates on methods of encoding, DCT, compression methods, security, etc. Different methodologies and network simulations have been analyzed here. Various methods of compression methodologies and its performance metrics has been investigated and presented in a table manner.
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: 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: Medical digital images usually have low resolution because of nature of their acquisition. Therefore, this paper focuses on zooming these images to obtain better level of information, required for the purpose of medical diagnosis. For this purpose, a strategy for selecting pixels in zooming operation is proposed. It is based on the principle of analog clock and utilizes a combination of point and neighborhood image processing. In this approach, the hour hand of clock covers the portion of image to be processed. For alignment, the center of clock points at middle pixel of the selected portion of image. The minute hand is longer in length, and is used to gain information about pixels of the surrounding area. This area is called neighborhood pixels region. This information is used to zoom the selected portion of the image. The proposed algorithm is implemented and its performance is evaluated for many medical images obtained from various sources such as X-ray, Computerized Tomography (CT) scan and Magnetic Resonance Imaging (MRI). However, for illustration and simplicity, the results obtained from a CT scanned image of head is presented. The performance of algorithm is evaluated in comparison to various traditional algorithms in terms of Peak signal-to-noise ratio (PSNR), maximum error, SSIM index, mutual information and processing time. From the results, the proposed algorithm is found to give better performance than traditional algorithms.
Abstract: The security of the medical images and its related data is the major research area which is to be concentrated in today’s era. Security in the medical image indicates that the physician may hide patients’ related data in the medical image and transfer it safely to a defined location using reversible watermarking. Many reversible watermarking methods had proposed over the decade. This paper enhances the security level in brain tumor images to hide the patient’s detail, which has to be conferred with other physician’s suggestions. The details or the information will be hidden in Non-ROI area of the image by using the block cipher algorithm. The block cipher uses different keys to extract the details that are difficult for the intruder to detect all the keys and to spot the details, which are the key advantage of this method. The ROI is the tumor area and Non-ROI is the area rest of ROI. The Non-ROI should not be spoiled in any cause and the details in the Non-ROI should be extracted correctly. The reversible watermarking method proposed in this paper performs well when compared to existing methods in the process of extraction of an original image and providing information security.
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: The aim of this paper is to propose a general
framework for storing, analyzing, and extracting knowledge from
two-dimensional echocardiographic images, color Doppler images,
non-medical images, and general data sets. A number of high
performance data mining algorithms have been used to carry out this
task. Our framework encompasses four layers namely physical
storage, object identification, knowledge discovery, user level.
Techniques such as active contour model to identify the cardiac
chambers, pixel classification to segment the color Doppler echo
image, universal model for image retrieval, Bayesian method for
classification, parallel algorithms for image segmentation, etc., were
employed. Using the feature vector database that have been
efficiently constructed, one can perform various data mining tasks
like clustering, classification, etc. with efficient algorithms along
with image mining given a query image. All these facilities are
included in the framework that is supported by state-of-the-art user
interface (UI). The algorithms were tested with actual patient data
and Coral image database and the results show that their performance
is better than the results reported already.
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: 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: Image segmentation plays an important role in
medical imaging applications. Therefore, accurate methods are
needed for the successful segmentation of medical images for
diagnosis and detection of various diseases. In this paper, we have
used maximum entropy to achieve image segmentation. Maximum
entropy has been calculated using Shannon, Renyi and Tsallis
entropies. This work has novelty based on the detection of skin lesion
caused by the bite of a parasite called Sand Fly causing the disease is
called Cutaneous Leishmaniasis.
Abstract: Content Based Image Retrieval (CBIR) coupled with
Case Based Reasoning (CBR) is a paradigm that is becoming
increasingly popular in the diagnosis and therapy planning of medical
ailments utilizing the digital content of medical images. This paper
presents a survey of some of the promising approaches used in the
detection of abnormalities in retina images as well in
mammographic screening and detection of regions of interest
in MRI scans of the brain. We also describe our proposed
algorithm to detect hard exudates in fundus images of the
retina of Diabetic Retinopathy patients.