Use of Image Processing Techniques to Automatically Diagnose Sickle-Cell Anemia Present in Red Blood Cells Smear

Sickle Cell Anemia is a blood disorder which results from the abnormalities of red blood cells and shortens the life expectancy to 42 and 48 years for males and females respectively. It also causes pain, jaundice, shortness of breath, etc. Sickle Cell Anemia is characterized by the presence of abnormal cells like sickle cell, ovalocyte, anisopoikilocyte. Sickle cell disease usually presenting in childhood, occurs more commonly in people from parts of tropical and subtropical regions where malaria is or was very common. A healthy RBC is usually round in shape. But sometimes it changes its shape to form a sickle cell structure; this is called as sickling of RBC. Majority of the sickle cells (whose shape is like crescent moon) found are due to low haemoglobin content. An image processing algorithm to automate the diagnosis of sickle-cells present in thin blood smears is developed. Images are acquired using a charge-coupled device camera connected to a light microscope. Clustering based segmentation techniques are used to identify erythrocytes (red blood cells) and Sickle-cells present on microscopic slides. Image features based on colour, texture and the geometry of the cells are generated, as well as features that make use of a priori knowledge of the classification problem and mimic features used by human technicians. The red blood cell smears were obtained from IG Hospital, Rourkela. The proposed image processing based identification of sickle-cells in anemic patient will be very helpful for automatic, sleek and effective diagnosis of the disease

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

Semantic segmentation of conjunctiva region for non-invasive anemia detection applications

Technology is changing the future of healthcare, technology-supported non-invasive medical procedures are more preferable in the medical diagnosis. Anemia is one of the widespread diseases affecting the wellbeing of individuals around the world especially childbearing age women and children and addressing this issue with the advanced technology will reduce the prevalence in large numbers. The objective of this work is to perform segmentation of the conjunctiva region for non-invasive anemia detection applications using deep learning. The proposed U-Net Based Conjunctiva Segmentation Model (UNBCSM) uses fine-tuned U-Net architecture for effective semantic segmentation of conjunctiva from the digital eye images captured by consumer-grade cameras in an uncontrolled environment. The ground truth for this supervised learning was given as Pascal masks obtained by manual selection of conjunctiva pixels. Image augmentation and pre-processing was performed to increase the data size and the performance of the model. UNBCSM showed good segmentation results and exhibited a comparable value of Intersection over Union (IoU) score between the ground truth and the segmented mask of 96% and 85.7% for training and validation, respectively

Automatic detection of white blood cancer from bone marrow microscopic images using convolutional neural networks

Leukocytes, produced in the bone marrow, make up around one percent of all blood cells. Uncontrolled growth of these white blood cells leads to the birth of blood cancer. Out of the three different types of cancers, the proposed study provides a robust mechanism for the classification of Acute Lymphoblastic Leukemia (ALL) and Multiple Myeloma (MM) using the SN-AM dataset. Acute lymphoblastic leukemia (ALL) is a type of cancer where the bone marrow forms too many lymphocytes. On the other hand, Multiple myeloma (MM), a different kind of cancer, causes cancer cells to accumulate in the bone marrow rather than releasing them into the bloodstream. Therefore, they crowd out and prevent the production of healthy blood cells. Conventionally, the process was carried out manually by a skilled professional in a considerable amount of time. The proposed model eradicates the probability of errors in the manual process by employing deep learning techniques, namely convolutional neural networks. The model, trained on cells' images, first pre-processes the images and extracts the best features. This is followed by training the model with the optimized Dense Convolutional neural network framework (termed DCNN here) and finally predicting the type of cancer present in the cells. The model was able to reproduce all the measurements correctly while it recollected the samples exactly 94 times out of 100. The overall accuracy was recorded to be 97.2%, which is better than the conventional machine learning methods like Support Vector Machine (SVMs), Decision Trees, Random Forests, Naive Bayes, etc. This study indicates that the DCNN model's performance is close to that of the established CNN architectures with far fewer parameters and computation time tested on the retrieved dataset. Thus, the model can be used effectively as a tool for determining the type of cancer in the bone marrow. © 2013 IEEE

Automated histopathological analyses at scale

Thesis: S.M., Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2017.Cataloged from PDF version of thesis.Includes bibliographical references (pages 68-73).Histopathology is the microscopic examination of processed human tissues to diagnose conditions like cancer, tuberculosis, anemia and myocardial infractions. The diagnostic procedure is, however, very tedious, time-consuming and prone to misinterpretation. It also requires highly trained pathologists to operate, making it unsuitable for large-scale screening in resource-constrained settings, where experts are scarce and expensive. In this thesis, we present a software system for automated screening, backed by deep learning algorithms. This cost-effective, easily-scalable solution can be operated by minimally trained health workers and would extend the reach of histopathological analyses to settings such as rural villages, mass-screening camps and mobile health clinics. With metastatic breast cancer as our primary case study, we describe how the system could be used to test for the presence of a tumor, determine the precise location of a lesion, as well as the severity stage of a patient. We examine how the algorithms are combined into an end-to-end pipeline for utilization by hospitals, doctors and clinicians on a Software as a Service (SaaS) model. Finally, we discuss potential deployment strategies for the technology, as well an analysis of the market and distribution chain in the specific case of the current Indian healthcare ecosystem.by Mrinal Mohit.S.M

Electrical Characterization and Detection of Blood Cells and Stones in Urine

Urine contains an immense amount of information related to its physical, chemical, and biological components; hence, it is a promising tool in detecting various diseases. Available methods for detecting hematuria (blood in the urine) are not accurate. Results are influenced by many factors, such as, health and vitals of the patients, settings of the equipment and laboratories, which leads to false positive or false negative outputs. This necessitates the development of new, accurate, and easy-access methods that save time and effort. This study demonstrates a label-free and accurate method for detecting the presence of red and white blood cells (RBCs and WBCs) in urine by measuring the changes in the dielectric properties of urine upon increasing concentrations of both cell types. The current method could detect changes in the electrical properties of fresh urine over a short time interval, making this method suitable for detecting changes that cannot be recognized by conventional methods. Correcting these changes enabled the detection of a minimum cell concentration of 10² RBCs per ml which is not possible by conventional methods used in the labs except for the semi-quantitative method that can detect 50 RBCs per ml, but it is a lengthy and involved procedure, not suitable for high volume labs. This ability to detect a very small amount of both types of cells makes the proposed technique an attractive tool for detecting hematuria, the presence of which is indicative of problems in the excretory system. Furthermore, urolithiasis is also a very common problem worldwide, affecting adults, kids, and even animals. Calcium oxalate is the major constituent of urinary tract stones in individuals, primarily due to the consumption of high oxalate foods. The occurrence of urinary oxalate occurs by endogenous synthesis, especially in the upper urinary tract. In a normal, healthy individual, the excretion of oxalate ranges from 10 to 45 mg/day, depending on the age and gender, but the risk of stone formation starts at 25 mg/day depending on the health history of the individual. This study also addresses the detection of the presence of calcium oxalate in urine following the same label-free approach. This can be done by measuring the changes in the dielectric properties of urine with increasing concentrations of calcium oxalate hydrate (CaC₂O₄.H₂O). The current method could detect dynamic changes in the electrical properties of urine over a time interval in samples containing calcium oxalate hydrate even at a concentration as low as 10 μg/mL of urine, making this method suitable for detecting changes that cannot be recognized by conventional methods. The ability to detect a very small amount of stones makes it an attractive tool for detecting and quantifying stones in kidneys. Using a non-invasive method which also works as a precautionary measure for early detection of some severe ailments, holds a good scope. It forms the basis of the cytological examinations and molecular assays for the diagnosis of several diseases. This method can be considered a point-of-care test because the results can be instantaneously shared with the members of the medical team. Based on these results, it is anticipated that the present approach to be a starting point towards establishing the foundation for label-free electrical-based identification and quantification of an unlimited number of nano-sized particles

Content aware multi-focus image fusion for high-magnification blood film microscopy

Automated digital high-magnification optical microscopy is key to accelerating biology research and improving pathology clinical pathways. High magnification objectives with large numerical apertures are usually preferred to resolve the fine structural details of biological samples, but they have a very limited depth-of-field. Depending on the thickness of the sample, analysis of specimens typically requires the acquisition of multiple images at different focal planes for each field-of-view, followed by the fusion of these planes into an extended depth-of-field image. This translates into low scanning speeds, increased storage space, and processing time not suitable for high-throughput clinical use. We introduce a novel content-aware multi-focus image fusion approach based on deep learning which extends the depth-of-field of high magnification objectives effectively. We demonstrate the method with three examples, showing that highly accurate, detailed, extended depth of field images can be obtained at a lower axial sampling rate, using 2-fold fewer focal planes than normally required

Tick-borne pathogens in African cattle – novel molecular tools for diagnostics in epizootiology and the genetics of resistance

Zecken-übertragene Pathogene gehören zu den schädlichsten Mikroorganismen, die für Verluste bei der Tierhaltung verantwortlich sind, mit einer erheblichen Bedrohung für die menschliche Bevölkerung. Ihr Nachweis mittels Blutausstrich oder Serologie erlaubt eher die Bestimmung einzelner Arten, während Ko-infektionen eher die Regel sind. Einschränkungen ergeben sich insbesondere in Laboren mit begrenzten Ressourcen und ohne nachhaltige Kapazität. Die vorliegende Arbeit beschäftigt sich mit der Identifizierung von 4 Gruppen von TBPs in Nordkamerun (Ehrlichien, Rickettsien, Spirochäten und Piroplasmen), wobei sieben Erstnachweise von Krankheitserregern in Rinderpopulationen (Borrelia theileri, Theileria mutans, T. velifera, Anaplasma platys, Anaplasma sp. ‘Hadesa’) in Kamerun sind, einschließlich den weltweiten Erstnachweisen von Rickettsia felis und Ehrlichia canis im Rind. Bei mehr als 80% (1123/1260, 89.1%) der untersuchten infizierten Rinder wurden mindestens zwei der vier untersuchten Genusgruppen (903/1123, 80.4%) von Krankheitserregern nachgewiesen, was die Einschränkung konventioneller Methoden unterstreicht. Über die Plattform eines kommerziellen Biochip-Anbieters (Chipron® in Berlin) wurde ein neuartiges Chip-basiertes Diagnostik-Array entwickelt. Dieses PCR-basierte Tool ermöglicht die gleichzeitige Identifizierung von fünf Erreger-Gattungen, einschließlich neuer Arten, im Blut mehrfach befallener Rinder. Deutlich mehr Krankheitserreger ließen sich so bei einer erhöhten Spezifität und Sensitivität nachweisen. Das LCD-Array kann problemlos in kleinen Veterinärlaboratorien in endemischen Ländern einschließlich Afrikas eingesetzt werden. Die häufigen Mischinfektionen mit je nach Region und Rinderrasse unterschiedlicher Zusammensetzung von Erregern werden zusätzlich von Umweltfaktoren beeinflusst (χ2, regression, p< 0,05). Unterschiedliche Reaktionen zwischen Individuen und Rassen aus derselben Umgebung motivierten den Test auf Heritabilität. Niedrige bis moderate (h_(obs.)^2=0.1 and h_(liab.)^2=0.6) Erbanlagen beobachtet, was eine genomische Grundlage für das Merkmal der Resistenz gegen Zecken-übertragene Pathogene darstellt. Dieses Ergebnis bestätigt die Möglichkeit einer Verbesserung der Resistenz durch Züchtung. Die genomweite Analyse erwies die quantitative Natur der Merkmale und verwies auf potentiell assoziierte genomische Regionen, von denen eine noch nicht in der Literatur beschrieben wurde. Erweiterte Analysen und eine größere Stichprobengröße wären nötig, um die Rinder Rassen besser zu charakterisieren (Feinkartierung von Lozi unter natürlicher Selektion und Allel-Fixierung).Les agents pathogènes transmis par les tiques comptent parmi les micro-organismes les plus nocifs responsables des pertes et de la détérioration des élevages, avec une menace importante pour la population humaine. Leur détection par frottis sanguin ou sérologie est plus susceptible de permettre l'identification d'espèces individuelle, alors que les co-infections sont plus fréquentes. Les contraintes sont tangibles dans les laboratoires aux ressources limitées et aux capacités non-durables. La présente thèse fait état de l'identification de sept agents pathogènes décris pour la première fois dans le cheptel bovin du Cameroun (Borrelia theileri, Theileria mutans, T. velifera, Anaplasma platys, Anaplasma sp. ‘Hadesa’) y compris ceux identifiés pour la première fois dans l'hôte bovin (Rickettsia felis et Ehrlichia canis). Plus de 80% de (1123/1260, 89.1%) la population infectée étudiée était co-infectée par au moins deux groupes des genres (903/1123, 80.4%) des agents pathogènes étudiés, ce qui souligne les limites des méthodes d'identification d'un seul agent pathogène précédemment utilisées. Sur la base de ces contraintes, une nouvelle matrice de diagnostic a été mise au point, grâce à la plate-forme du fournisseur commercial de biochip Chipron® à Berlin, en Allemagne. L'outil (LCD-array) basé sur la PCR a permis l'identification simultanée d'échantillons co-infectés, y compris les nouvelles espèces. Il a également permis d'identifier un plus grand nombre de microorganismes en état de co-infection avec une spécificité et une sensibilité accrues. Cet outil peut facilement être utilisé dans des laboratoires vétérinaires à capacité réduite dans les pays endémiques d'Afrique et d’ailleurs. Il a été démontré que la co-infection ainsi que la combinaison de pathogènes responsables diffèrent selon les zones climatiques et les populations bovines, influencées par des facteurs environnementaux (χ2, régression). Des réponses différentes entre individus et espèces (p< 0.05) d'un même environnement ont motivé le test des valeurs d'héritabilité. Des héritabilités faibles à modérées ont été décelées (h_(obs.)^2=0.1 and h_(liab.)^2=0.6), impliquant un fondement génétique du caractère de résistance. Ce résultat confirme la possibilité d'amélioration des facultés d’adaptation du bétail par gestion du système de production animale. Les analyses génétiques ont révélé les portions du génome responsables des phénotypes variés. Des analyses approfondies sur un plus grand échantillon seront nécessaires pour une différenciation des populations par représentation précise des loci sous sélection naturelle.Tick-borne pathogens are among the most harmful micro-organisms responsible for losses in animal husbandry, with a significant threat to the human population. Their detection by blood smear or serology is more likely to allow the identification of individual species, whereas co-infections are more common. Limitations arise especially in laboratories with limited resources and without sustainable capacities. The present thesis presents the identification of seven organisms in the cattle population from Cameroon for the first time (Borrelia theileri, Theileria mutans, T. velifera, Anaplasma platys, Anaplasma sp. ‘Hadesa’) including the first published reports of Rickettsia felis and Ehrlichia canis in cattle worldwide. More than 80% of the infected studied population (1123/1260, 89.1%) were found being co-infected with at least two of the four studied groups of genera (903/1123, 80.4%), highlighting the caveats of the predominating single pathogen identification approach. Based on those observed limitations, a novel chip-based diagnostic array was developed through the platform of the commercial biochip manufacturer Chipron® in Berlin, Germany. The PCR-based tool allowed the simultaneous identification of co-infected samples of five genera, including novel species. Moreover, the array allowed the identification of significantly more pathogens in co-infection with increased specificity and sensitivity compared to traditional Sanger sequencing. This LCD-array can be easily implemented in small veterinary laboratories in endemic countries of Africa and elsewhere. The co-infection status and pathogen combinations was proven to differ between climatic zones and cattle populations, and being influenced by environmental factors (χ2, regression). Different responses between individuals and breeds (p< 0.05) from the same environment motivated the test for heritability values. The observed low to moderate heritability based on genotyping dataset (h_(obs.)^2=0.1 and h_(liab.)^2=0.6) implied a genomic foundation of the trait of resistance to tick pathogens. More importantly, this result confirmed the possibility of improvement by breeding, which may be implemented as a control measure. The genome-wide analysis revealed the quantitative nature of the traits of resistance, exposing putative associated genomic regions with one of them not yet reported in the literature. Extended analyses and larger sample size will be advantageous for population differentiation and breed improvement through fine mapping of loci under natural selection and allele fixation related to resistance and susceptibility traits