Red blood cell segmentation and classification method using MATLAB

Red blood cells (RBCs) are the most important kind of blood cell. Its diagnosis is very important process for early detection of related disease such as malaria and anemia before suitable follow up treatment can be proceed. Some of the human disease can be showed by counting the number of red blood cells. Red blood cell count gives the vital information that help diagnosis many of the patient’s sickness. Conventional method under blood smears RBC diagnosis is applying light microscope conducted by pathologist. This method is time-consuming and laborious. In this project an automated RBC counting is proposed to speed up the time consumption and to reduce the potential of the wrongly identified RBC. Initially the RBC goes for image pre-processing which involved global thresholding. Then it continues with RBCs counting by using two different algorithms which are the watershed segmentation based on distance transform, and the second one is the artificial neural network (ANN) classification with fitting application depend on regression method. Before applying ANN classification there are step needed to get feature extraction data that are the data extraction using moment invariant. There are still weaknesses and constraints due to the image itself such as color similarity, weak edge boundary, overlapping condition, and image quality. Thus, more study must be done to handle those matters to produce strong analysis approach for medical diagnosis purpose. This project build a better solution and help to improve the current methods so that it can be more capable, robust, and effective whenever any sample of blood cell is analyzed. At the end of this project it conducted comparison between 20 images of blood samples taken from the medical electronic laboratory in Universiti Tun Hussein Onn Malaysia (UTHM). The proposed method has been tested on blood cell images and the effectiveness and reliability of each of the counting method has been demonstrated

A switched-beam antenna for cellular communication

Wireless communication has created a continuing demand for increased bandwidth and better quality of services. Smart antenna arrays are one of the ways to accommodate this demand which can provide numerous benefits to service provider and the customer. Switched-beam antenna was chosen for this project due to its easier implementation and lower cost compared to adaptive array. Switched-beam antenna is one of smart antenna technique which comprises a number of predefined beams. The control system switches among the beams that provide the maximum signal response. Through the investigation and study on this system, found that, the 1200 sectorization with three monopole antenna elements suited for prototype construction. The initial stage to design this system is by using MA TLAB simulation to identify the antenna characteristic and the parameters involved. The second stage is about the construction of the prototype switched-beam antenna used to measure the antenna gain and relative power level which displayed using CASSY program

Automated Low-Cost Malaria Detection System in Thin Blood Slide Images Using Mobile Phones

Malaria, a deadly disease which according to the World Health Organisation (WHO) is responsible for the fatal illness in 200 million people around the world in 2010, is diagnosed using peripheral blood examination. The work undertaken in this research programme aims to develop an automated malaria parasite-detection system, using microscopic-image processing, that can be incorporated onto mobile phones. In this research study, the main objective is to achieve the performance equal to or better than the manual microscopy, which is the gold standard in malaria diagnosis, in order to produce a reliable automated diagnostic platform without expert intervention, for the effective treatment and eradication of the deadly disease. The work contributed to the field of mathematical morphology by proposing a novel method called the Annular Ring Ratio transform for blood component identification. It has also proposed an automated White Blood Cell and Red Blood Cell differentiation algorithm, which when combined with ARR transform method, has wide applications not only for malaria diagnosis but also for many blood related analysis involving microscopic examination. The research has undertaken investigations on infected cell identification which aids in the calculation of parasitemia, the measure of infection. In addition, an automated diagnostic tool to detect the sexual stage (gametocytes) of the species P.falciparum for post-treatment malaria diagnosis was developed. Furthermore, a parallel investigation was carried out on automated malaria diagnosis on fluorescent thin blood films and a WBC and infected cell differentiation algorithm was proposed. Finally, a mobile phone application based on the morphological image processing algorithms proposed in this thesis was developed. A complete malaria diagnostic unit using the mobile phones attached to a portable microscope was set up which has enormous potential not only for malaria diagnosis but also for the blood parasitological field where advancement in medical diagnostics using cellular smart phone technology is widely acknowledged

Automatic Detection and Quantification of WBCs and RBCs Using Iterative Structured Circle Detection Algorithm

Segmentation and counting of blood cells are considered as an important step that helps to extract features to diagnose some specific diseases like malaria or leukemia. The manual counting of white blood cells (WBCs) and red blood cells (RBCs) in microscopic images is an extremely tedious, time consuming, and inaccurate process. Automatic analysis will allow hematologist experts to perform faster and more accurately. The proposed method uses an iterative structured circle detection algorithm for the segmentation and counting of WBCs and RBCs. The separation of WBCs from RBCs was achieved by thresholding, and specific preprocessing steps were developed for each cell type. Counting was performed for each image using the proposed method based on modified circle detection, which automatically counted the cells. Several modifications were made to the basic (RCD) algorithm to solve the initialization problem, detecting irregular circles (cells), selecting the optimal circle from the candidate circles, determining the number of iterations in a fully dynamic way to enhance algorithm detection, and running time. The validation method used to determine segmentation accuracy was a quantitative analysis that included Precision, Recall, and F-measurement tests. The average accuracy of the proposed method was 95.3% for RBCs and 98.4% for WBCs

A PCNN Framework for Blood Cell Image Segmentation

This research presents novel methods for segmenting digital blood cell images under a Pulse Coupled Neural Network (PCNN) framework. A blood cell image contains different types of blood cells found in the peripheral blood stream such as red blood cells (RBCs), white blood cells (WBCs), and platelets. WBCs can be classified into five normal types – neutrophil, monocyte, lymphocyte, eosinophil, and basophil – as well as abnormal types such as lymphoblasts and others. The focus of this research is on identifying and counting RBCs, normal types of WBCs, and lymphoblasts. The total number of RBCs and WBCs, along with classification of WBCs, has important medical significance which includes providing a physician with valuable information for diagnosis of diseases such as leukemia. The approach comprises two phases – segmentation and cell separation – followed by classification of WBC types including detection of lymphoblasts. The first phase presents two methods based on PCNN and region growing to segment followed by a separate method that combines Circular Hough Transform (CHT) with a separation algorithm to find and separate each RBC and WBC object into separate images. The first method uses a standard PCNN to segment. The second method uses a region growing PCNN with a maximum region size to segment. The second phase presents a WBC classification method based on PCNN. It uses a PCNN to capture the texture features of an image as a sequence of entropy values known as a texture vector. First, the parameters of the texture vector PCNN are defined. This is then used to produce texture vectors for the training images. Each cell type is represented by several texture vectors across its instances. Then, given a test image to be classified, the texture vector PCNN is used to capture its texture vector, which is compared to the texture vectors for classification. This two-phase approach yields metrics based on the RBC and WBC counts, WBC classification, and identification of lymphoblasts. Both the standard and region growing PCNNs were successful in segmenting RBC and WBC objects, with better accuracy when using the standard PCNN. The separate method introduced with this research provided accurate WBC counts but less accurate RBC counts. The WBC subimages created with the separate method facilitated cell counting and WBC classification. Using a standard PCNN as a WBC classifier, introduced with this research, proved to be a successful classifier and lymphoblast detector. While RBC accuracy was low, WBC accuracy for total counts, WBC classification, and lymphoblast detection were overall above 96%

Computer aided diagnosis algorithms for digital microscopy

Automatic analysis and information extraction from an image is still a highly chal- lenging research problem in the computer vision area, attempting to describe the image content with computational and mathematical techniques. Moreover the in- formation extracted from the image should be meaningful and as most discrimi- natory as possible, since it will be used to categorize its content according to the analysed problem. In the Medical Imaging domain this issue is even more felt because many important decisions that affect the patient care, depend on the use- fulness of the information extracted from the image. Manage medical image is even more complicated not only due to the importance of the problem, but also because it needs a fair amount of prior medical knowledge to be able to represent with data the visual information to which pathologist refer. Today medical decisions that impact patient care rely on the results of laboratory tests to a greater extent than ever before, due to the marked expansion in the number and complexity of offered tests. These developments promise to improve the care of patients, but the more increase the number and complexity of the tests, the more increases the possibility to misapply and misinterpret the test themselves, leading to inappropriate diagnosis and therapies. Moreover, with the increased number of tests also the amount of data to be analysed increases, forcing pathologists to devote much time to the analysis of the tests themselves rather than to patient care and the prescription of the right therapy, especially considering that most of the tests performed are just check up tests and most of the analysed samples come from healthy patients. Then, a quantitative evaluation of medical images is really essential to overcome uncertainty and subjectivity, but also to greatly reduce the amount of data and the timing for the analysis. In the last few years, many computer assisted diagno- sis systems have been developed, attempting to mimic pathologists by extracting features from the images. Image analysis involves complex algorithms to identify and characterize cells or tissues using image pattern recognition technology. This thesis addresses the main problems associated to the digital microscopy analysis in histology and haematology diagnosis, with the development of algorithms for the extraction of useful information from different digital images, but able to distinguish different biological structures in the images themselves. The proposed methods not only aim to improve the degree of accuracy of the analysis, and reducing time, if used as the only means of diagnoses, but also they can be used as intermediate tools for skimming the number of samples to be analysed directly from the pathologist, or as double check systems to verify the correct results of the automated facilities used today

Computer aided diagnosis algorithms for digital microscopy

Automatic analysis and information extraction from an image is still a highly chal- lenging research problem in the computer vision area, attempting to describe the image content with computational and mathematical techniques. Moreover the in- formation extracted from the image should be meaningful and as most discrimi- natory as possible, since it will be used to categorize its content according to the analysed problem. In the Medical Imaging domain this issue is even more felt because many important decisions that affect the patient care, depend on the use- fulness of the information extracted from the image. Manage medical image is even more complicated not only due to the importance of the problem, but also because it needs a fair amount of prior medical knowledge to be able to represent with data the visual information to which pathologist refer. Today medical decisions that impact patient care rely on the results of laboratory tests to a greater extent than ever before, due to the marked expansion in the number and complexity of offered tests. These developments promise to improve the care of patients, but the more increase the number and complexity of the tests, the more increases the possibility to misapply and misinterpret the test themselves, leading to inappropriate diagnosis and therapies. Moreover, with the increased number of tests also the amount of data to be analysed increases, forcing pathologists to devote much time to the analysis of the tests themselves rather than to patient care and the prescription of the right therapy, especially considering that most of the tests performed are just check up tests and most of the analysed samples come from healthy patients. Then, a quantitative evaluation of medical images is really essential to overcome uncertainty and subjectivity, but also to greatly reduce the amount of data and the timing for the analysis. In the last few years, many computer assisted diagno- sis systems have been developed, attempting to mimic pathologists by extracting features from the images. Image analysis involves complex algorithms to identify and characterize cells or tissues using image pattern recognition technology. This thesis addresses the main problems associated to the digital microscopy analysis in histology and haematology diagnosis, with the development of algorithms for the extraction of useful information from different digital images, but able to distinguish different biological structures in the images themselves. The proposed methods not only aim to improve the degree of accuracy of the analysis, and reducing time, if used as the only means of diagnoses, but also they can be used as intermediate tools for skimming the number of samples to be analysed directly from the pathologist, or as double check systems to verify the correct results of the automated facilities used today

Estado da arte das técnicas de contagem de elementos específicos em imagens digitais.

Contagem de células. Contagem de bactérias e/ou colônias de bactérias. Contagem de árvores. Contagem de pessoas. Contagem de frutas. Contagem de estruturas específicas em amostras de solo. Contagem de colônias de fungos. Contagem de pólen. Contagem de espigas. Contagem de cromossomos. Contagem de ovos de Aedes Aegypti. Contagem de defeitos em madeira. Contagem detos. Contagem de peixes. Contagem de grãos. Contagem de esperma. Contagem de parasitas de malária. Contagem de plâncton. Contagem de larvas. Contagem de lesões causadas por cisticercose. Contagens em ovários. Contagem de pontos fluorescentes em células. Contagem de biscoitos com defeito. Contagem de elementos geológicos extraplanetários. Contagem de sedimentos na urina. Contagem de partículas de amianto. Contagem de trilhas de radição. Contagem de pintas na pele. Contagem de tarugos de aço. Contagem de circuitos impressos. Contagem de fontes de raios gama. Contagem de automóveis. Contagem de rubis em relógios. Contagem de tramas em quadros de pinturas. Contagem de objetos multicoloridos. Contagens gerais. Avaliação de desempenho dos algoritmos.bitstream/item/63197/1/documento120.pd

Characterising HIV-associated Mycobacterium tuberculosis blood stream infection

Despite the success of antiretroviral therapy roll-out, one-million people still die with HIV-infection annually. In high-burden settings, tuberculosis remains the most common proximal cause of hospital admission and death in people living with HIV. In post-mortem series, 90% of fatal HIV-associated tuberculosis is ‘disseminated’. This is a form of tuberculosis which has been poorly characterised and, despite the high associated-mortality, never been the subject of interventional trials to define optimal treatment strategies. This thesis contends that the mode of severe HIV-associated tuberculosis is blood stream infection. First it is argued with reference to historical literature that blood stream dissemination is part of the natural history of post-primary tuberculosis infection, and that HIV-associated M. tuberculosis blood stream infection (MTBBSI) can be conceived of as a reversion to, and exaggerated form of this natural history. Using data from a large cohort (n=571) of HIV-infected inpatients with CD4 cell count <350 cells/mm3 and a new TB diagnosis from Khayelitsha Hospital, South Africa (the KDHTB study), the extent and magnitude of MTBBSI is shown to be a major determinant of clinical phenotype and mortality risk. Systematic, quantitative markers of blood stream dissemination, including TB blood culture, urine-lipoarabinomannan (uLAM), and urine GeneXpert MTB/RIF testing (uXpert), can be combined into a ‘disseminated TB score. KDHTB patients have high prevalence of abnormal sodium and fluid balance, metabolic acidosis associated with acute kidney injury, hyperlactataemia, infiltrative liver and splenic pathology, and anaemia. Each of these pathophysiologies in turn correlates to disseminated TB score, and to risk of death, suggesting bacterial burden and MTBBSI are central to the pathophysiology of severe HIV-associated tuberculosis. An individual patient data meta-analysis, with 20 independent data sets comprising over 6000 patients, is used to establish the prevalence of TB blood culture positive disease amongst critically unwell HIV-infected inpatients. This shows that MTBBSI is more common than previous estimates suggest, is a strong independent association with mortality risk, and is also associated with specific increased risk of death if empirical treatment is delayed. The development of tools to identify and measure MTBBSI is described, including Xpert-ultra testing of blood, and the use of a novel dye, DMN-trehalose, to perform direct microscopy on patient blood samples. These techniques are used to provide the first description of the pharmacodynamics of MTBBSI, by serially quantifying blood bacilli load over the first 72-hours of standard TB therapy, in 28 patients with high predicted probability of bacteraemia. In this cohort, risk of mortality is related to several summary measures of MTBBSI dynamics in the first 72-hours of therapy, suggesting this approach can be used to define biomarkers of treatment response. In conclusion, MTBBSI is a highly-specific diagnosis responsible for substantial mortality in hospitalised people living with HIV. Interventions with strengthened bacteriocidal activity, focussed on reducing bacterial burden, are warranted for MTBBSI. Tools developed in this thesis, including potential pharmacodynamic biomarkers, should facilitate such trials

Search for Host Factors Involved in Attachment of Agrobacterium Tumefaciens to Plants

Agrobacterium tumefaciens is able to infect a diverse array of plants and causes crown gall disease. Typically these bacteria attach to plant roots and transform the plant cells to induce tumors. The mechanism of this attachment in the infection process is not yet fully understood. Using wild type Arabidopsis thaliana, Columbia-0, and several Arabidopsis mutant lines as a binding target, we screened for A. thaliana mutants with altered adhesion. The A. thaliana mutant lines were selected in The Arabidopsis Information Resource (TAIR) according to possible location of the resulting protein and similarity to known transformation mutants. Of these mutants nine showed a variation in attachment from the wild type, of which two were known transformation mutants rat1 and rat3. Of these, the two were higher and seven were lower. Two mutants showed a growth phenotype with one having more roots and the other having wavy root hair growth, but both had wildtype attachment. I also attempted to quantify the adhesion in these mutants using several approaches. However, I was not able to find a quantitative method that correlated well with microscopic observations of adhesion. Real-time PCR (qPCR) assay showed measurable differences between the mutants lines and the wildtype, suggesting some effect of the mutation on the interaction of A. thaliana and A. tumefaciens. Using this assay the level of bacterial attachment to the root surface can be indirectly measured. In the process of selecting this method several other approaches were attempted. These included flow cytometry of bacterial cells and of cells bound to beads, 96-well plate binding assay and the previously used plate colony counting. Mutants used in this study were also evaluated for transformation efficiency. Most of the mutants had not been previously tested for attachment or transformation. The attachment and transformation phenotypes provide a better understanding of the gene that has been affected by these mutant Arabidopsis lines. The affected gene sequence and the data available on that gene were used to analyze the functional domains of the proteins showing an altered phenotype. There should be specific results here, rather than generalizations. These showed that kinase, extensin and heat shock protein domains were present in low attachment mutants and fasciclin, CDC48 and VirB2 domains were in high attachment mutants. The leucine-rich repeat (LRR) domains were strongly represented in all of the attachment mutants.. The SALK_ 040891C and SALK_085076C mutants that had high clumping but low attachment had heat shock, extensin and LRR domains. The putative protein functional domains may give insight to the possible function of the gene in both Arabidopsis and in possible interaction with A. tumefaciens. From these phenotypes, along with bioinformatic analysis, we can analyze mutant plant lines that exhibit enhanced or inhibited attachment. The combination of these methods may yield insight on the attachment mechanism as well as the infection process as a whole