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Cloud-based systems involve the management and control on the level of delivered service, which is crucial in order to maintain service continuity and customers’ trust for the service provided. Predefined service level agreement (SLA) is normally used as the main element in managing the cloud-based system. However, such SLA basically cannot be adjusted during operations. Current studies on SLA adjustment are mainly focused on modifying the agreement before commencing operations. The cloud environment is, however, dynamic and requires occasional review of the SLA parameters to manage expectations. Thus, to maintain service continuity that is acceptable to all parties, SLA adjustment is necessary during service operations. This paper presents a real-time and proactive SLA renegotiation model to support the dynamic nature of cloud-based systems. Renegotiation is shown for four different scenarios based on parameter weightage. In order to achieve real-time decision, a multi-offer generation approach is used. A novel mechanism to detect and predict service violation is proposed to ensure proactive renegotiation. Simulation is performed to verify the model. In the simulation, the model is found to be effective as it is able to generate multi-offers in a single renegotiation round and reduce the impact of service violations during service operations.

Eye-related disease such as diabetic retinopathy (DR) is a medical ailment in which the retina of the human eye is smashed because of damage to the tiny retinal blood vessels in the retina. Ophthalmologists identify DR based on various features such as the blood vessels, textures and pathologies. With the rapid development of methods of analysis of biomedical images and advanced computing techniques, image processing-based software for the detection of eye disease has been widely used as an important tool by ophthalmologists. In particular, computer vision-based methods are growing rapidly in the field of medical images analysis and are appropriate to advance ophthalmology. These tools depend entirely on visual analysis to identify abnormalities in Retinal Fundus images. During the past two decades, exciting improvement in the development of DR detection computerised systems has been observed. This paper reviews the development of analysing retinal images for the detection of DR in three aspects: automatic algorithms (classification or pixel to pixel methods), detection methods of pathologies from retinal fundus images, and extraction of blood vessels of retinal fundus image algorithms for the detection of DR. The paper presents a detailed explanation of each problem with respect to retinal images. The current techniques that are used to analyse retinal images and DR detection issues are also discussed in detail and recommendations are made for some future directions.

Most medical images suffer from inadequate contrast and brightness, which leads to blurred or weak edges (low contrast) between adjacent tissues resulting in poor segmentation and errors in classification of tissues. Thus, contrast enhancement to improve visual information is extremely important in the development of computational approaches for obtaining quantitative measurements from medical images. In this research, a contrast enhancement algorithm that applies gray-level S-curve transformation technique locally in medical images obtained from various modalities is investigated. The S-curve transformation is an extended gray level transformation technique that results into a curve similar to a sigmoid function through a pixel to pixel transformation. This curve essentially increases the difference between minimum and maximum gray values and the image gradient, locally thereby, strengthening edges between adjacent tissues. The performance of the proposed technique is determined by measuring several parameters namely, edge content (improvement in image gradient), enhancement measure (degree of contrast enhancement), absolute mean brightness error (luminance distortion caused by the enhancement), and feature similarity index measure (preservation of the original image features). Based on medical image datasets comprising 1937 images from various modalities such as ultrasound, mammograms, fluorescent images, fundus, X-ray radiographs and MR images, it is found that the local gray-level S-curve transformation outperforms existing techniques in terms of improved contrast and brightness, resulting in clear and strong edges between adjacent tissues. The proposed technique can be used as a preprocessing tool for effective segmentation and classification of tissue structures in medical images. Display Omitted A novel S-curve gray level transformation is presented for contrast enhancement.Technique works based on the difference between adjacent high and low gray values.Contrast enhancement preserves the original gray scale of most medical images modality.Developed method is prominent in improving the performance of segmentation algorithms.Qualitative and Quantitative analysis is proven satisfactory.

The chapter aims at analyzing the impact of induced-mood states on working-memory functions of the prefrontal cortex. We would like to propose functional near-infrared spectroscopy (fNIRS) as alternative neuroimaging modality for functional magnetic resonance imaging to allow a more naturalistic settings. fNIRS allows the subject to sit comfortably (neuroergonomics) and therefore it is less likely to randomly modulate the subject's emotion, suitable for study related to psychology. 31 participants were chosen to undergo the n-back task paradigm and the results were recorded.

Electronic version: ; Kumar, Dileep, 1985- ; Optical imaging for biomedical and clinical applications. ; Boca Raton : Taylor & Francis / CRC Press, 2018 ; 9781315351384 ; (OCoLC)1008868285.

Near-infrared spectroscopy (NIRS), one of the candidates to be used in a neurofeedback system or brain-computer interface (BCI), measures the brain activity by monitoring the changes in cerebral hemoglobin concentration. However, hemodynamic changes in the scalp may affect the NIRS signals. In order to remove the superficial signals when NIRS is used in a neurofeedback system or BCI, real-time processing is necessary. Real-time scalp signal separating (RT-SSS) algorithm, which is capable of separating the scalp-blood signals from NIRS signals obtained in real-time, may thus be applied. To demonstrate its effectiveness, two separate neurofeedback experiments were conducted. In the first experiment, the feedback signal was the raw NIRS signal recorded while in the second experiment, deep signal extracted using RT-SSS algorithm was used as the feedback signal. In both experiments, participants were instructed to control the feedback signal to follow a predefined track. Accuracy scores were calculated based on the differences between the trace controlled by feedback signal and the targeted track. Overall, the second experiment yielded better performance in terms of accuracy scores. These findings proved that RT-SSS algorithm is beneficial for neurofeedback.

Synopsis Objective Melanin is a major skin colour pigment that made up of eumelanin (the dark brown–black colour) and pheomelanin (the light red–yellow colour) pigments. Skin-whitening products typically contain depigmentation agents that reduce the level of pigmentation by changing the pheomelanin–eumelanin production. Similarly, in skin pigment treatment of skin disorders, the melanin production is managed accordingly. To assess and improve treatment efficacy, it is important to have a measurement tool that is capable of determining the melanin types objectively. So far, the efficacy assessment is subjective. In this study, an inverse skin reflectance pigmentation analysis system that determines eumelanin and pheomelanin content is developed and evaluated in an observational study involving 36 participants with skin photo type IV. Methods The reflectance spectra of the left forearms of participants were analysed by the pigmentation analysis system to determine their skin parameters – pheomelanin and eumelanin concentrations, melanosome volume fraction, and epidermal thickness. The determined skin parameters are then inputted into the realistic skin model (RSM) of the Advanced Systems Analyses Program (asap®) to generate the ground truth reflectance spectra for the given skin parameters to validate the system. Results The developed pigmentation analysis system is found to be accurate with a spectral error of 0.0163 ± 0.009 between measured reflectance and the reflectance output of the analysis system and RSM. The regression analysis shows a strong linear relationship (R2 = 0.994) indicating good precision. The relative concentrations of pheomelanin (38.23 ± 15.04) and eumelanin (1.68 ± 0.91) analysed by the system gives a ratio of pheomelanin to eumelanin of 0.048 ± 0.029; this value is consistent with previously reported figure of 0.049. Conclusion The proposed pigmentation analysis system is able to determine melanin types and their relative concentrations. It has the potential to assess the efficacy of the skin-whitening and pigmentation treatments objectively in a non-invasive manner. Resume Objectif La melanine est un pigment majeur de couleur de la peau composee d’ eumelanine (de couleur brun-noir fonce) et de pheomelanin (de couleur rouge – jaune clair). Les produits pour blanchir la peau contiennent generalement des agents de depigmentation qui reduisent le niveau de la pigmentation en changeant la production de pheomelanin – eumelanine. De meme, dans le traitement de troubles de la pigmentation de la peau, la production de melanine est geree en consequence. Pour evaluer et ameliorer l'efficacite du traitement, il est important d'avoir un outil de mesure qui permet de determiner les types de melanine objectivement. Jusqu’a present, l’ evaluation de l'efficacite est subjective. Dans cette etude, un systeme d'analyse de la reflexion inverse de la pigmentation de la peau qui determine l'eumelanine et la teneur en pheomelanin est developpe et evalue dans une etude observationnelle portant sur 36 participants avec le type de photo de peau IV. Methodes Les spectres de reflectance de l'avant-bras gauche de participants ont ete analyses par le systeme d'analyse de la pigmentation de determiner les parametres de la peau : concentrations en pheomelanine et eumelanine, volume partiel des melanosomes, et l’epaisseur de l’epiderme. Les parametres determines de la peau sont ensuite entres dans le modele de peau realiste (RSM) de l’ analyse du programme Advanced Systems (asap®) pour generer les spectres de reflectance vraie de base pour les parametres donnes de la peau afin de valider le systeme. Resultats Le systeme d'analyse de la pigmentation developpe est precis avec une erreur spectrale de 0.0163 ± 0.009 entre reflectance mesuree et le resultat de l'analyse de la reflectance par le systeme d'analyse RSM. L'analyse de regression montre une forte relation lineaire (R2 = 0.994) indiquant une bonne precision. Les concentrations relatives des pheomelanin (38.23 ± 15.04) et l'eumelanine (1.68 ± 0.91) analysees par le systeme donnent un ratio de pheomelanin a l'eumelanine de 0.048 ± 0.029; cette valeur est compatible avec le chiffre precedemment rapporte de 0.049. Conclusion Le systeme d'analyse de la pigmentation propose est capable de determiner les types de melanine et leurs concentrations relatives. Il a le potentiel d’evaluer l'efficacite des traitement de blanchiment de la peau, d'une maniere non – invasive et objective.

Images captured with insufficient illumination generally have dark shadows and low contrast. This problem seriously affects other forms of image processing schemes such as face detection, security surveillance, image fusion. In this paper, a new image enhancement algorithm using the important features of the contourlet transform is presented. A new transformation function is developed based on the existing sigmoid function and the tanh functions which have very interesting properties in enhancing images which are suffering from low illuminations or non-uniform lighting conditions. Literature dictates that contourlet transform has better performance in representing the image salient features such as edges, lines, curves, and contours than wavelets for its anisotropy and directionality and is therefore well suited for multiscale edge-based image enhancement. The algorithm works for gray scale and color images. For a color image, it is first converted from RGB (red, green, and blue) to HSI (hue, saturation, and intensity) color model. Then, the intensity component of the HSI color space is adjusted the preserving the original color using a new nonlinear transformation function. The simulation results show that this approach gives encouraging results for images taken in low-light and/or non-uniform lighting conditions. The results obtained are compared with other enhancement algorithms based on wavelet transform, curvelet transform, bandlet transform, histogram equalization (HE), and contrast limited adaptive histogram equalization. The performance of the enhancement based on the contourlet transform method is superior. The algorithm is checked for a total of 151 test images. A total of 120 of them are used for subjective evaluation and 31 are used for objective evaluation. For over 90 % of the cases, the system is superior over the other enhancement methods.

In this paper, it is proposed that the mapping of uncertainties of the three predominant physiographic features of terrains, which are mountain, basins and piedmont slopes, using variation in the spatial resolution over which these landforms are defined, can be performed with fuzzy classification. The proposed methodology allows for the generation of fuzzy certainty maps which assign high levels of uncertainty to regions with high levels of change across scales. This paper demonstrates that fuzzy certainty maps provide a better quantification of landform character than Boolean landform maps alone. In terms of sensitivity to noise, the methodology is able to identify narrow bridges, and spurious landforms, and assign these errors with low certainty values. However, it is unable to identify spurious modifications to landform shape, with these errors being assigned high certainty values. Ground truth maps are required to identify these errors.

In this paper, a modification of the algorithm proposed by Ahmad Fadzil et al. (2011) for surface roughness computation from digital elevation models (DEMs) via multiscale analysis is presented. The new algorithm takes into account that the three predominant physiographic features of terrains (mountains, basins and piedmont slopes) have distinct curvature region distributions and hence, distinct roughness characteristics. To this end, the surface roughness of individual cells of DEMs is computed by identifying the curvature region distribution and roughness characteristics of each individual mountain, basin and piedmont slope region. The modified algorithm allows for the localisation and quantification curvature regions over varying scales for specific regions, providing a more appropriate surface roughness parameter.

Introduction: This paper presents a comprehensive review of acne grading and measurement. Acne is a chronic disorder of the pilosebaceous units, with excess sebum production, follicular epidermal hyperproliferation, inflammation and Propionibacterium acnes activity. Most patients are affected with acne vulgaris, which is the prevalent type of acne. Acne vulgaris consists of comedones (whitehead and blackhead), papules, pustules, nodules and cysts. Objectives: To review and identify the issues for acne vulgaris grading and computational assessment methods. To determine the future direction for addressing the identified issues. Methods: There are two main methods of assessment for acne severity grading, namely, lesion counting and comparison of patient with a photographic standard. For the computational assessment method, the emphasis is on computational imaging techniques. Results: Current acne grading methods are very time consuming and tedious. Generally, they rely on approximation for counting lesions and hence the assessment is quite subjective, with both inter and intra-observer variability. It is important to accurately assess acne grade to evaluate its severity as this influences treatment selection and assessment of response to therapy. This will further help in better disease management and more efficacious treatment. Conclusion: Semi-automated or automated methods based on computational imaging techniques should be devised for acne grade assessment.

Most existing image enhancement algorithms work on a single image. Their performance is limited to the capacity of the sensor by which the image is taken. In some cases they completely fail to provide us the necessary enhancements. This paper proposes a composite image approach for enhancing still images. The approach proposed combines the relevant features of the input images and produce a composite image which is rich in information content for human eye. The input images are first decomposed into multiple resolutions by using the contourlet transform which provides a better representation than the conventional transforms. Transformed coefficients are combined with a predefined fusion rules. The resultant image is found by performing inverse contourlet transformation of the composite image. The results found are encouraging and the algorithm does not introduce any distortion for applications in low light and/or non uniform lighting conditions. The composite image also contains almost all of the salient features of the input images

Retinal vasculature is a network of vessels in the retinal layer. In ophthalmology, information of retinal vasculature in analyzing fundus images is important for early detection of diseases related to the retina, e.g. diabetic retinopathy. However, in fundus images the contrast between retinal vasculature and the background is very low. As a result, analyzing or visualizing tiny retinal vasculature is difficult. There-fore, enhancement of retinal vasculature in digital fundus image is important to provide better visualization of retinal blood vessels as well as to increase accuracy of retinal vasculature segmentation. Fluorescein angiogram overcomes this imaging problem but it is an invasive procedure that leads to other physiological problems. In this research work, the low contrast problem of retinal fundus images ob-tained from fundus camera is addressed. We develop a fundus image model based on probability distribution function of melanin, haemoglobin and macular pigment to represent melanin, retinal vasculature and macular region, respectively. We determine retinal pigments makeup, namely macular pigment, melanin and haemoglobin using independent component analysis. Independent component image due to haemoglobin obtained is used since it exhibits higher contrast retinal vasculature. Contrast of reti-nal vasculature from independent component image due to haemoglobin is compared to those from other enhancement methods. Results show that this approach outperforms other non-invasive enhancement methods, such as contrast stretching, histogram equalization and CLAHE and can be beneficial for retinal vasculature segmentation. Contrast enhancement factor up to 2.62 for a digital retinal fundus image model is achieved. This improvement in contrast reduces the need of applying contrasting agent on patients.

Neuroimaging studies have shown that negative mood states will impair the working memory (WM) functions that affect haemodynamic conditions at the prefrontal cortex (PFC) region. Compared to other neuroimaging tools, haemodynamic responses in surrounding brain surfaces can be assessed feasibly and with less constrained using Optical Topography (OT). This paper presents near infrared spectroscopy (NIRS) signal analysis technique and OT brain haemodynamic assessment methods to investigate sensorimotor and working memory tasks, without biasing of mood. The continuous OT measurements for finger-tapping task and N-back tasks should result in significant task activation on the lateral motor cortex region and PFC region respectively. Findings from the first study confirmed the phenomena of contralateral motor cortex activation. In the second study, it is found that larger haemodynamic responses are associated with N-back WM task with higher difficulty.

Ulcer refers to a discontinuity of the skin exhibiting complete loss of the epidermis that is not short lived. The duration of the ulcer could last from a few weeks to even a few years. Patients suffering from such chronic skin ulcers face a huge loss of quality of life. The patients not only have to endure pain, but also time-consuming outpatient treatment and costs. Leg ulcers are very common, and based on the statistics, 3.5% of all adults in the United States suffer from venous leg ulcers [1]. Chronic ulcerative skin lesions affect about 1.5% of the European population and represent an important medical and social problem [2].

Drug addiction is one of the chronic mental disorders that results in a compulsive behavior to seek and consume drug. Recent statistics show that about 15% of world population is addicted with some sort of drug. It has been reported that drug addiction causes structural such as change in volume of gray and/or white matter, and functional neuroplasticity such as changes in the concentration of neurotransmitter (dopamine, glutamate, GABA etc.). Functional neuroplasticity can either block or excite neurotransmission among the neurons, hence altering the functionality of neurons and disrupt the control mechanism in the reward pathway and finally leading to an uncontrolled behavior. In drug addiction, the occurrence of uncontrolled behaviors in the different regions of reward pathways can be modeled using brain circuit to map directly the neural activities. This paper discusses the biological & chemical processes and control mechanisms involved in drug addiction develops a model of brain circuit under the influence of drug. It is observed that reduced inhibitory neurotransmission of GABA and increased excitatory neurotransmission of Glutamate in different regions of brain circuit contributes to reorganization of control mechanism by impaired encoding in control regions (PFC and Cigulate Gyrus). As a result, the drug related reward is considered as reinforcement of drug taking. Furthermore, it is also argued that estimation and quantification of GABA and Glutamate may provide significant differences between normal and drug addict brain that will lead to development of drug addiction severity monitoring system.

Medical imaging research deals with large, heterogeneous and fragmented amounts of medical images. Such challenges lead to the need for secure, federated and functional medical image storage within this research community. With limited IT professional and research fund, the community is better provided with an outsourcing arrangement. Cloud computing promises lower cost, high scalability and availability which can be a natural solution such problems in long-term medical image archive. This paper presents a private cloud storage design and prototype development within an organization to solve such issues. Leveraging on the ability of cloud computing is shown meet to the system requirements. The prototype is implemented on OwnCloud cloud storage framework. The complete functionality of OwnCloud made it an ideal platform to develop and deploy this kind of cloud-based system. OwnCloud can keep images in different file formats and share such images to other researchers in simple way.

Diagnosing retinal diseases of the eye requires analysing tiny retinal vessels. Digital colour fundus images are plagued by the problem of low and varied contrast. Further, with noise being present in the images, retinal vasculature is difficult to be analysed. This paper discusses various denoising methods to improve the SNR of the retinal fundus images before further image enhancement. By selecting a suitable method for denoising, the image's details are not lost as well as the contrast is maintained. Based on the performance of several techniques for denoising fundus images, it was found that the Time Domain Constraint Estimator (TDCE) showed a greater performance in the PSNR improvement of retinal fundus images. By reducing noise using TDCE, the performance of non-invasive methods for enhancing fundus images can be significantly improved without any loss of the details of the images.