Fourier–Mellin registration of two hyperspectral images

Hyperspectral images contain a great amount of information which can be used to more robustly register such images. In this article, we present a phase correlation method to register two hyperspectral images that takes into account their multiband structure. The proposed method is based on principal component analysis, the multilayer fractional Fourier transform, a combination of log-polar maps, and peak processing. The combination of maps is aimed at highlighting some peaks in the log-polar map using information from different bands. The method is robust and has been successfully tested for any rotation angle with commonly used hyperspectral scenes in remote sensing for scales of up to 7.5× and with pairs of hyperspectral images taken on different dates by the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) sensor for scales of up to 6.0×This work was supported in part by the Consellería de Cultura, Educación e Ordenación Universitaria [grant numbers GRC2014/008 and ED431G/08] and Ministry of Education, Culture and Sport, Government of Spain [grant numbers TIN2013-41129-P and TIN2016-76373-P] both are co-funded by the European Regional Development Fund (ERDF)S

GPU Accelerated FFT-Based Registration of Hyperspectral Scenes

Registration is a fundamental previous task in many applications of hyperspectrometry. Most of the algorithms developed are designed to work with RGB images and ignore the execution time. This paper presents a phase correlation algorithm on GPU to register two remote sensing hyperspectral images. The proposed algorithm is based on principal component analysis, multilayer fractional Fourier transform, combination of log-polar maps, and peak processing. It is fully developed in CUDA for NVIDIA GPUs. Different techniques such as the efficient use of the memory hierarchy, the use of CUDA libraries, and the maximization of the occupancy have been applied to reach the best performance on GPU. The algorithm is robust achieving speedups in GPU of up to 240.6×This work was supported in part by the Consellería de Cultura, Educacion e Ordenación Universitaria under Grant GRC2014/008 and Grant ED431G/08 and in part by the Ministry of Education, Culture and Sport, Government of Spain under Grant TIN2013-41129-P and Grant TIN2016-76373-P. Both are cofunded by the European Regional Development Fund. The work of A. Ordóñez was supported by the Ministry of Education, Culture and Sport, Government of Spain, under an FPU Grant FPU16/03537S

Glycerophospholipid oxidation and production of aldehydes in oesophageal adenocarcinoma

Oesophageal adenocarcinoma (OAC) has an unmet clinical need, with five year survival in the UK remaining at 15%. There has been little improvement with advances in surgical practice or systemic chemotherapeutic regimens. Early diagnosis holds the key to radical treatment, the clinical utility of breath testing has highlighted aldehydes as a potential early marker of cancer. The emerging field of lipidomics has identified variations in lipid composition between cancer and benign tissue. These observed changes have highlighted phospholipids as particularly important class responsible for structural membrane stability, cell signalling and replication. In this research, multiple mass spectrometry techniques were implemented to identify and correlate lipid abundance with increased aldehyde quantitation. Desorption Electrospray Ionisation- Mass Spectrometry (DESI-MS) was utilised for lipid profiling in oesophageal adenocarcinoma tissue to reveal a prevalence of Phosphatidic acids (PA) and Phosphatidylglycerol (PG) species. Comprehensive bioinformatics analysis highlighted the PG pathway with significantly dysregulation and positive phenotype to PG production. The investigation of aldehydes was performed in vivo by lipid oxidation and corroborated in OAC tissue by a targeted Liquid Chromatography mass spectrometry (LC-MS) method. This identified medium and long chain aldehydes (Pentanal, Nonanal, Un-decanal) at particularly increased concentration. To investigate the lipid product correlation, the chemistry of lipid oxidation was defined and characterised. To explored the origin of the increased PA and PG a targeted LC-MS method was created and patient tissue and surface mucus samples were collected at paired sites. The analysis confirmed a relative increase of PAs and PGs in OAC tissue and mucus of representative intensities suggesting a correlation between mucus sampling and cell phospholipid concentration. These data highlight the Phospholipid products of a genetically dysregulated pathway in OAC, which may contribute to the production of unstable polyunsaturated lipids which are prone to oxidation and formation of aldehydes.Open Acces

Novel medical imaging technologies for processing epithelium and endothelium layers in corneal confocal images. Developing automated segmentation and quantification algorithms for processing sub-basal epithelium nerves and endothelial cells for early diagnosis of diabetic neuropathy in corneal confocal microscope images

Diabetic Peripheral Neuropathy (DPN) is one of the most common types of diabetes that can affect the cornea. An accurate analysis of the corneal epithelium nerve structures and the corneal endothelial cell can assist early diagnosis of this disease and other corneal diseases, which can lead to visual impairment and then to blindness. In this thesis, fully-automated segmentation and quantification algorithms for processing and analysing sub-basal epithelium nerves and endothelial cells are proposed for early diagnosis of diabetic neuropathy in Corneal Confocal Microscopy (CCM) images. Firstly, a fully automatic nerve segmentation system for corneal confocal microscope images is proposed. The performance of the proposed system is evaluated against manually traced images with an execution time of the prototype is 13 seconds. Secondly, an automatic corneal nerve registration system is proposed. The main aim of this system is to produce a new informative corneal image that contains structural and functional information. Thirdly, an automated real-time system, termed the Corneal Endothelium Analysis System (CEAS) is developed and applied for the segmentation of endothelial cells in images of human cornea obtained by In Vivo CCM. The performance of the proposed CEAS system was tested against manually traced images with an execution time of only 6 seconds per image. Finally, the results obtained from all the proposed approaches have been evaluated and validated by an expert advisory board from two institutes, they are the Division of Medicine, Weill Cornell Medicine-Qatar, Doha, Qatar and the Manchester Royal Eye Hospital, Centre for Endocrinology and Diabetes, UK

Histopathological Analysis of Prostatic Adenocarcinoma and the Role of Immunohistochemistry in Distinction between Low Grade and High Grade Carcinomas

INTRODUCTION: Prostatic adenocarcinoma accounts for about 95% of malignancies of prostate gland. It is the most common malignant tumor among men. Incidence increases from 20% in men in their 50's to 70% in men between ages of 70's and 80's. The relative distribution of prostate cancer is different in each zone, 68% arises in the peripheral zone, 24% in transitional zone, and 8% in the central zone. Both genetic and environmental factors play a role in the origin of this disease. Long term androgen exposure and estrogen receptor β play an important role in the initiation of prostate cancer. Gleason's grading system is used to grade prostate cancers. Gleason's score ranges from 2-10. Serum prostate specific antigen is used to assist the diagnosis and management of prostate cancer. Immunohistochemical markers like cyclin D1, Ki-67 and ER-β are used to distinguish between low and high grade prostate adenocarcinomas. Alcian blue stain is used to demonstrate acid mucin production by tumor cells. AIMS AND OBJECTIVES: 1. To find out the age distribution of prostate carcinoma. 2. To distinguish between low grade and high grade prostate adenocarcinoma using Gleason's scoring method as well as immunohistochemical markers like Ki-67, cyclin D1, estrogen receptor -β. 3. To demonstrate the production of acid mucin by prostate cancer using histochemical stain like Alcian blue. 4. To correlate the level of serum prostate specific antigen with the grade of the tumor. MATERIALS AND METHODS: This is a study done in Thanjavur Medical College and Hospital from January 2015 to June 2017. Specimems obtained by transurethral resection of prostate from 50 cases were studied in the Department of Pathology. 1. Clinical details of patients like age, symptoms, location of the lesion within the prostate gland, serum PSA level were noted. 2. TURP specimens were fixed in 10% neutral buffered formalin, processed in a routine manner and sections were stained with hematoxylin and eosin. The sections were examined under light microscope and using modified Gleason's grading system. The prostate adenocarcinomas were grouped into low grade (score ≤6) and high grade tumors (score ≥7). 3. Immunohistochemistry was performed on whole tissue section using Cyclin D1, Ki67 and ER-b. 4.Also special stain like combined alcian blue -PAS was used to demonstrate the acid mucin production by prostate adenocarcinomas. RESULTS: Out of the 50 cases, 60% of the cases were between the age group of 70 - 80 years of age. Peripheral zone was involved by the tumor in all the 50 cases. There was a steep rise in the serum PSA level with increase in the Gleason's score. Mean serum PSA level in cases with low Gleason's score was 27.9 ng/mL .This was lower than serum PSA level of individuals with high Gleason score in whom it was 139.25 ng/mL. All 16 cases with high grade tumors (100%) showed cyclin D1 positivity. The mean expression of cyclin D1 was higher (35%) in high grade tumors than low grade tumors (16%). 94% of the high grade tumors showed Ki-67 expression when compared to low grade tumors where the expression of Ki-67 was found only in 29 % of the cases. ER-b expression was only 7% in high grade tumors when compared to the low grade tumors where the mean ER-b expression was 30.58%. 59% of the well differentiated tumors showed acid mucin production. None of the poorly differentiated tumors secreted acid mucin. CONCLUSION: Thus, a steep rise was found in the serum prostate specific antigen levels with increase in Gleason's score. Also a significant correlation was found between Gleason's score and cyclin D1, Ki-67 expression. The expression of these two markers was higher in high grade prostate adenocarcinomas. ER-β expression was higher in low grade tumors and its expression gets reduced with increase in Gleason's score. Acid mucin production by tumor cells gets reduced as the Gleason's score of the tumor increases

Sparse algorithms for decoding and identification of neural circuits

The brain, as an information processing machine, surpasses any man-made computational device, both in terms of its capabilities and its efficiency. Neuroscience research has made great strides since the foundational works of Cajal and Golgi. However, we still have very little understanding about the algorithmic underpinnings of the brain as an information processor. Identifying mechanistic models of the functional building blocks of the brain will have significant impact not just on neuroscience, but also on artificial computational systems. This provides the main motivation for the work presented in this thesis, summarily i) biologically-inspired algorithms that can be efficiently implemented in silico, ii) functional identification of the processing in certain types of neural circuits, and iii) a collaborative ecosystem for brain research in a model organism, towards the synergistic goal of understanding functional mechanisms employed by the brain. First, this thesis provides a highly parallelizable, biologically-inspired, motion detection algorithm that is based upon the temporal processing of the local (spatial) phase of a visual stimulus. The relation of the phase based motion detector to the widely studied Reichardt detector model, is discussed. Examples are provided comparing the performance of the proposed algorithm with the Reichardt detector as well as the optic flow algorithm, which is the workhorse for motion detection in computer vision. Further, it is shown through examples that the phase based motion detection model provides intuitive explanations for reverse-phi based illusory motion percepts. Then, tractable algorithms are presented for decoding with and identification of neural circuits, comprised of processing that can be described by a second-order Volterra kernel (quadratic filter). It is shown that the Reichardt detector, as well as models of cortical complex cells, can be described by this structure. Examples are provided for decoding of visual stimuli encoded by a population of Reichardt detector cells and complex cells, as well as their identification from observed spike times. Further, the phase based motion detection model is shown to be equivalent to a second-order Volterra kernel acting on two normalized inputs. Subsequently, a general model that computes the ratio of two non-linear functionals, each comprising linear (first order Volterra kernel) and quadratic (second-order Volterra kernel) filters, is proposed. It is shown that, even under these highly non-linear operations, a population of cells can encode stimuli faithfully using a number of measurements that are proportional to the bandwidth of the input stimulus. Tractable algorithms are devised to identify the divisive normalization model and examples of identification are provided for both simulated and biological data. Additionally, an extended framework, comprising parallel channels of divisively normalized cells each subjected to further divisive normalization from lateral feedback connections, is proposed. An algorithm is formulated for identifying all the components in this extended framework from controlled stimulus presentation and observed outputs samples. Finally, the thesis puts forward the Fruit Fly Brain Observatory (FFBO), an initiative to enable a collaborative ecosystem for fruit fly brain research. Key applications in FFBO, and the software and computational infrastructure enabling them, are described along with case studies

Optical Coherence Tomography and Its Non-medical Applications

Optical coherence tomography (OCT) is a promising non-invasive non-contact 3D imaging technique that can be used to evaluate and inspect material surfaces, multilayer polymer films, fiber coils, and coatings. OCT can be used for the examination of cultural heritage objects and 3D imaging of microstructures. With subsurface 3D fingerprint imaging capability, OCT could be a valuable tool for enhancing security in biometric applications. OCT can also be used for the evaluation of fastener flushness for improving aerodynamic performance of high-speed aircraft. More and more OCT non-medical applications are emerging. In this book, we present some recent advancements in OCT technology and non-medical applications

First-in-human controlled inhalation of thin graphene oxide nanosheets to study acute cardiorespiratory responses

Graphene oxide nanomaterials are being developed for wide-ranging applications but are associated with potential safety concerns for human health. We conducted a double-blind randomized controlled study to determine how the inhalation of graphene oxide nanosheets affects acute pulmonary and cardiovascular function. Small and ultrasmall graphene oxide nanosheets at a concentration of 200 μg m−3 or filtered air were inhaled for 2 h by 14 young healthy volunteers in repeated visits. Overall, graphene oxide nanosheet exposure was well tolerated with no adverse effects. Heart rate, blood pressure, lung function and inflammatory markers were unaffected irrespective of graphene oxide particle size. Highly enriched blood proteomics analysis revealed very few differential plasma proteins and thrombus formation was mildly increased in an ex vivo model of arterial injury. Overall, acute inhalation of highly purified and thin nanometre-sized graphene oxide nanosheets was not associated with overt detrimental effects in healthy humans. These findings demonstrate the feasibility of carefully controlled human exposures at a clinical setting for risk assessment of graphene oxide, and lay the foundations for investigating the effects of other two-dimensional nanomaterials in humans. Clinicaltrials.gov ref: NCT03659864

Rapid-Scan EPR and Imaging

EPR imaging at low frequency is a powerful tool to obtain important biological information in vivo in a non-invasive way. Properties of nitroxide and trityl radical imaging reagents have been studied. Developments in rapid scan imaging techniques are reported that improve efficiency of experiments and user-friendliness of software. Relaxation and signal-to-noise ratio (S/N) in pulse experiments on trityl radicals were measured at frequencies between 400 MHz and 1.5 GHz. Relaxation time increases as the frequency increases and the radical concentration decreases. Since relaxation time is a sensitive and accurate measure of oxygen pressure, this study provides criteria for the selection of the frequencies for in vivo applications. Rapid-scan EPR of irradiated solids at L-band was studied. The results show that for the same data acquisition time, S/N for rapid-scans was significantly higher than for conventional continuous wave spectra. Rapid scan EPR imaging of nitroxide was performed at 250 MHz. Experimental parameters for the sinusoidal single-sweep method were varied to get better image quality. The results show that larger gradient strength provides higher spatial resolution while smaller gradient step size provides finer texture. Another method based on field-stepped linear-scans was developed; field step size, rapid-scan segment width, rapid-scan frequencies and some other parameters were varied. The field-stepped linear-scan method was compared with the sinusoidal single-sweep method using criteria including linewidth and S/N, and the former turned out to be a less effective alternative to the latter. New developments have been made to expand what can be achieved with EPR imaging, such as reduced data acquisition time, quantification of image features, efficient use of instrument time, and simplified experimental procedures from data acquisition to spectral analysis. The Python programming language was used successfully as a new and comprehensive approach to run EPR imaging experiments compared to the prior method that used multiple software packages. These developments will make EPR imaging more accessible for a much wider user group

Multisite adaptive computation offloading for mobile cloud applications

The sheer amount of mobile devices and their fast adaptability have contributed to the proliferation of modern advanced mobile applications. These applications have characteristics such as latency-critical and demand high availability. Also, these kinds of applications often require intensive computation resources and excessive energy consumption for processing, a mobile device has limited computation and energy capacity because of the physical size constraints. The heterogeneous mobile cloud environment consists of different computing resources such as remote cloud servers in faraway data centres, cloudlets whose goal is to bring the cloud closer to the users, and nearby mobile devices that can be utilised to offload mobile tasks. Heterogeneity in mobile devices and the different sites include software, hardware, and technology variations. Resource-constrained mobile devices can leverage the shared resource environment to offload their intensive tasks to conserve battery life and improve the overall application performance. However, with such a loosely coupled and mobile device dominating network, new challenges and problems such as how to seamlessly leverage mobile devices with all the offloading sites, how to simplify deploying runtime environment for serving offloading requests from mobile devices, how to identify which parts of the mobile application to offload and how to decide whether to offload them and how to select the most optimal candidate offloading site among others. To overcome the aforementioned challenges, this research work contributes the design and implementation of MAMoC, a loosely coupled end-to-end mobile computation offloading framework. Mobile applications can be adapted to the client library of the framework while the server components are deployed to the offloading sites for serving offloading requests. The evaluation of the offloading decision engine demonstrates the viability of the proposed solution for managing seamless and transparent offloading in distributed and dynamic mobile cloud environments. All the implemented components of this work are publicly available at the following URL: https://github.com/mamoc-repo