Automated Thalassemia cell image segmentation using hybrid Fuzzy C-Means and K-Means

Thalassemia is a form of hereditary disease. Thalassemia is one of the world's most common illnesses. The morphology of red blood cells is most affected by this disorder. This research proposes a new method of automatically segmenting red blood cells from microscopic blood smear images. The research suggests a novel combination of image processing techniques and extensive preprocessing to achieve superior segmentation performance. In this work, the eleven designated color spaces, with six filters and three contrasts enhancing, Fuzzy c-means and K-means segmentation studied using five evaluation parameters. This evaluation is based on the ground truth image. The Photoshop program performs novel ground truth techniques for multi-object sense (RBC cells). The optimization of all image processing stages was obtained through local image datasets (258 images) obtained from seven thalassemia patients in the Erbil – thalassemia center and five samples of normal blood cells in Children Raparin Teaching Hospital. The image was captured with different light intensities (low, medium, high) and with /without a yellow filter in Biophysics Research lab /Education College / Salahaddin University –Erbil. This study found that the best light intensity for image slide capture utilizing a microscope was medium without using a yellow filter with an accuracy of 0.91± 0.14 and a performance of 95.34%

Klasifikasi Eritrosit Pada Thalasemia Minor Menggunakan Fitur Konvolusi dan Multi-Layer Perceptron

 Thalassemia blood disorder is a condition that can affect the blood's ability to function normally and can lead to erythropoiesis. In this blood disorder, there are nine types of abnormal erythrocytes, namely elliptocytes, pencils, teardrops, acanthocytes, stomatocytes, targets, spherocytes, hypochromic and normal. At present, thalassemia examination is carried out using Hb electrophoresis and is done manually so it will be subjective and take a long time. Therefore, this research implements the Convolutional Neural Network (CNN) and Multi-Layer Perceptron (MLP) algorithms. This study aims to determine the performance of convolution features as image feature extraction and MLP as an image classification method and then implemented on NVIDIA Jetson Nano. The convolution features used in this study apply the CNN VGG16 architecture. Then model learning is carried out on 7245 data by configuring hyperparameters. The best accuracy with the hyperparameter configuration is a batch that is 16, the epoch is 400, the learning rate is 0.0001, the dropout1 layer is 0.1 and the dropout2 layer is 0.1. From this configuration it produces optimal accuracy at 96.175%. In the following, the model that has been made is then implemented on the NVIDIA Jetson Nano as a mobile media to be applied to the medical world resulting in an average prediction speed for each class of 48.330 seconds. The obtained performance time and accuracy are suitable for use by medical personnel to predict the class of abnormal erythrocytes

IMPLEMENTASI MASK-RCNN PADA DATASET KECIL CITRA SEL DARAH MERAH BERDASARKAN KRITERIA WARNA SEL

Examination of red blood cell morphology is one of the diagnostic aids for several diseases, one of which is anemia. The development of the application of digital image processing technology, artificial intelligence, and computer-assisted diagnosis opens opportunities to solve various problems related to medical images. Red blood cells sticking together or overlapping is a challenge in the red blood cell segmentation process which ultimately affects the results of cell type identification. A method that can perform instance segmentation is needed to overcome this problem. This study aims to implement the Mask-RCNN algorithm on a small red blood cell image dataset and evaluate the prediction results' performance. Based on the research results, the attached red blood cells can be detected individually by the model, and the accuracy of the cell detection results is 68.27%. Mask-RCNN can be used for blood cell segmentation instances and blood cell detection on small datasets, but the model accuracy still needs to be improved. Therefore it is necessary to do further research by increasing the number of datasets used.Pemeriksaan morfologi sel darah merah merupakan salah satu alat bantu penegakan diagnosis pada beberapa penyakit, salah satunya anemia. Perkembangan penerapan teknologi pengolahan citra digital, kecerdasan artifisial dan computer-aided diagnosis membuka peluang untuk menyelesaikan berbagai permasalahan terkait citra medis. Sel darah merah yang saling menempel atau bertumpuk merupakan tantangan dalam proses segmentasi sel darah merah yang pada akhirnya berpengaruh pada hasil pengenalan jenis sel. Metode yang dapat melakukan instance segmentation sangat diperlukan untuk mengatasi masalah tersebut. Penelitian ini bertujuan untuk mengimplementasikan algoritma Mask-RCNN pada dataset kecil citra sel darah merah dan mengevaluasi performa hasil prediksi. Berdasarkan hasil penelitian sel-sel darah merah yang menempel dapat dideteksi secara individual oleh model dan akurasi hasil deteksi sel adalah 68,27%. Mask-RCNN dapat digunakan untuk instance segmentasi sel darah dan deteksi sel darah pada dataset kecil namun akurasi model masih perlu ditingkatkan. oleh sebab itu perlu dilakukan penelitian selanjutnya dengan menambah jumlah dataset yang digunakan

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

Detecting Red Blood Cells Morphological Abnormalities Using Genetic Algorithm and Kmeans

Vision is the most advanced of our senses, so it is not surprising that images play the single most important role in human perception. Computer-aided diagnosis is another important application of pattern recognition, aiming at assisting doctors in making diagnostic decisions. Many diseases which are not blood diseases in origin have hematological abnormalities and manifestation (have symptoms appeared on the blood). CBC (cell blood count test) for instance, is still the first test to be requested by the physicians or become in their mind. Blood abnormality can be in white blood cells, red blood cells and plasma. In this thesis, red blood cells are the suggested for detecting it is abnormality. The abnormality of blood cells shapes can't be detected easily, where the CBC (cell blood count) device give a count number and percentages not a description of the shapes of the blood cells, when the blood cells shapes wanted to be known, hematologist asked to view the blood films under the microscope which is time consuming task besides that the human error risk is high. Since the number of abnormal cells to normal cells in a given blood sample give a measure of the disease severity, detecting one cell with potential abnormality can give premature warning for future illness that can be avoided or treated earlier. This case can't be detected by hematologist. Computer involved in such task to save time and effort besides minimizing human error. This thesis name is "DETECTING RED BLOOD CELLS MORPHOLOGICAL ABNORMALITIES USING GENETIC ALGORITHM AND KMEANS". In this thesis, the thesis divided into four phases. First phase data collection where blood samples was drawn from healthy and sick people and then blood films made and viewed under microscope and an images captured for these blood films. Second phase preprocessing phase where the images prepared for the next phase. Third phase feature extraction was executed where these features are spatial domain and frequency domain features. Fourth phase is the classification phase where the features fed into the classifier to be classified. An acceptable detection rate is achieved by the proposed system. The genetic algorithm classifier success rate was 92.31% and the K-means classifier success rate was 94.00%

Internal Medicine: Hematology

In this study guide there is the presentation of up-to-date data of etiology, pathogenesis, diagnostic and treatment criteria of hematological diseases such as anemia, hemoblastosis, lymphadenopathy, splenomegaly, hemorrhagic diathesis, disseminated intravascular coagulation syndrome, etc. The study guide is designed for senior students, interns, masters, postgraduate students and clinical interns in the specialties "Therapy", "General practice – family medicine"

Flow Morphometry of Red Blood Cell Storage Quality Based on Neural Networks

Red blood cell transfusion is routinely performed to improve tissue oxygenation in patients with decreased hemoglobin levels and oxygen-carrying capacity. Generally, blood banks process and store packed red blood cells as RCCs. During storage, RBCs undergo progressive biochemical and morphological changes which are collectively described as storage lesion. According to regulatory guidelines, the quality of RCCs is assessed by quantifying hemolysis before transfusion. However, the hemolysis level only gives an indication of the already lysed erythrocytes; it does not indicate the degree of deterioration of aged cells, which are known to compromise the post-transfusion survival. Morphological analysis, a method that has the potential to provide a simple and practical diagnosis, is suitable for indicating the degradation of RBCs and thus has considerable power to predict actual post-transfusion survival. Microfluidic systems with suspended RBCs can enable fully automated morphological diagnosis based on image analysis with large cell statistics and high sample throughput. The previous version of the flow morphometry system, which was based on a binary decision tree was able to show in a first attempt that spherocytes are a suitable candidate for such a morphological storage lesion marker. However, due to the low classification resolution (three morphology classes), possible shear-induced morphology changes of the measurement system could not be evaluated. In this study, the image classification of the flow morphometry system was substantially enhanced by using a convolutional neural network to strongly improve the resolution and accuracy of the morphology classification. The resulting CNN-based classification achieved a high overall accuracy of 92% with RBCs being classified into nine morphology classes. Through this improved classification resolution, it was possible to assess degradation-induced morphologies at high resolution simultaneously with shear-induced morphologies in RCCs. The overall goal was to provide a robust and strong marker for storage lesion that reflects post-transfusion survival of RBCs. Therefore, it was necessary to analyze the extent to which the shear in the microfluidic system affected the morphological transients between RBC classes. Indeed, it could be shown that shear-induced morphology changes appear dependent on the position of the focal plane height in the flow chamber. The proportion of stomatocytes is increased near the surfaces of the laminar flow chamber. This temporary shear-induced morphology transformation can occur only in flexible erythrocytes with intact membrane properties. Therefore, these cells should be considered a subset of healthy erythrocytes that can reversibly alter from stomatocyte to discocyte morphology. The nine RBC morphology classes of the improved classification resolution were further analyzed to determine whether they exhibit a particular pattern based on their relative proportions during storage that could be used as a storage lesion marker. All individual RBC classes, except for the spherical morphologies, undergo reversible transitions among themselves that are related to the SDE sequence and result in a low signal-to-noise ratio. The proportions of the irreversible spherical morphologies, spheroechinocytes and spherocytes, were defined as the lesion index. This lesion index showed a strong correlation to hemolysis levels. In fact, the correlation between the hemolysis level and the lesion index was so good that it persisted at an individual RCC level. A preliminary lesion index threshold of 11.1% could be established, which is equivalent to a hemolysis threshold of 0.8% established in regulatory guidelines, to assess whether an RCC is of appropriate quality for transfusion. However, the lesion index, besides predicting the hemolysis level, can also be used to generate more information about post-transfusion survival, since it consists exclusively of the RBC morphologies that are removed by the body in a very short time after transfusion in the recipient. Finally, we translated the newly established lesion index and standard biochemical parameters into a quality assessment of RCC shipped and transported repeatedly on air rescue missions to assess an eventual deterioration of the RBCs. We showed that the quality of RCCs was not inferior to control samples after repeated air rescue missions during storage. German regulations allow RCCs to be stored for 42 days in a temperature range of +2°C to +6°C. Compliance with this regulation can be secured during air rescue missions by means of suitable logistics based on a rotation system. By using efficient cooling devices, the logistics and maintenance of the thermal conditions are both safe and feasible. A well-defined rotation system for the use of RCCs during routine air rescue missions offers a resource-saving option and enables the provision of RCCs in compliance with German transfusion guidelines. This innovative concept enables life-saving prehospital transfusions directly at the incident scene. CNN-based flow morphometry and the calculated lesion index allow a reliable assessment of RCC quality. The method also decreases the demand for complex laboratory procedures. Therefore, it is highly advisable to include the lesion index as an additional marker for storage lesion in routine clinical practice. Unlike hemolysis, the lesion index may serve as a good indicator of post-transfusion survival. Thus, both measurements together could provide increased safety and efficacy of stored RCCs

Automated assessment of erythrocyte parameters using artificial neural network.

No abstract available.The original print copy of this thesis may be available here: http://wizard.unbc.ca/record=b131706

Evaluation of Ayabringaraja Paanidham in the Management of Veluppu Noi (Anaemia)

Nutritional deficiency diseases affect the humankind with adverse impacts on their socio-economic status. Even with greater advancements in biomedical research, certain diseases are riddled with inconsistent treatment modalities and difference of opinion among practitioners or researchers. Anaemia pertains to be one such disease condition with multi-faceted treatment modalities. Globally, anaemia remains as a persistent health problem due to various known and unknown factors. It is one of the common public health problems that indirectly affects the socio-economic status of populations in both developed and developing countries. Although the primary cause is iron deficiency, more frequently it coexists with a number of other causes, such as malaria, parasitic infections, malnutrition and haemoglobinopathies. Iron deficiency affects at least one third of the world’s population or two billion persons and is therefore, a critical nutritional problem worldwide. The Physio-chemical analysis of the trial drug Aya Bhringaraja Paanidham has revealed its safety through the results observed found to be with in normal limits as per WHO standards. The results of Toxicity studies (Acute and Sub-Acute) were found safe thereby ensuring the safety of trial drug in experimental animal models. The Pharmacological screening has shown that the trial drug has Erythropoeitic and Hepatoprotective activity in experimental animal models. The Clinical Trial has shown that ABP is effective, safe and cost effective in treating IDA patients. The improvements in the diagnostic features, both clinical and hematological were significant with no adverse alterations in Liver and Renal function tests. This study has validated the traditional formulation ABP for its safety and efficacy in human participants. The trial drug ABP is found to be economical and safe in the management of Veluppu Noi / Anaemia

Evaluation of Ayabringaraja Paanidham in the Management of Veluppu Noi (Anaemia)

Anaemia being a global public health issue has made a major impact on socio-economic development in both developing and developed countries. Since, the management of anaemia is a decisive issue worldwide, WHO encourages traditional medicines to establish a safe, effective and economic drug for the same. Siddha system includes many herbal and herbo-mineral formulations which are therapeutically used in anaemia. Among these, iron based formulations play an important role. Aya Bringaraja Paanidham (ABP) is one such formulation which has a unique combination of herbs and processed iron. The aim of this study was to evaluate the safety and efficacy of a Siddha herbomineral drug, Aya Bringaraja Paanidham (ABP) in the management of Veluppu noi (Anaemia). The primary objective to achieve this aim was to evaluate the pharmacological and clinical efficacy of ABP. In addition, secondary objective was to determine the toxicity of ABP in experimental animal models and human participants. To reach these objectives, the basic objective was to prepare the trial formulation, ABP and to standardize the same using modern techniques. The study was done in three parts: first part involving identification and authentication of botanical and other raw materials, followed by preparation of ABP; second, the preclinical studies including Physicochemical analysis Pharmacological safety and efficacy studies; third, the clinical trial to evaluate the efficacy and safety of ABP. The botanical and other raw materials used for the preparation of ABP were collected, identified and authenticated. After confirming their quality, the formulation was prepared as per Siddha scriptures, in strict adherence to GMP guidelines, under direct supervision using the infrastructure of a private Siddha Medicine manufacturing company (GMP certified). The physicochemical analysis including organoleptic characters, ash values, extractive values, Microbial content, High performance thin layer chromatography (HPTLC), High performance liquid chromatography (HPLC), Gas chromatography (GCMS) and Atomic absorption spectrometry (AAS) were done at the Central Research Facility, Sri Ramachandra University, Chennai. The physicochemical standards for ABP was established using these techniques. In addition, to know the status of iron in ABP, a non destructive analytical method, Electron spectroscopic chemical analysis (ESCA) was done at Indian Institute of Chemical Technology (CSIR), Hyderabad. The ionic state of iron in the sample was determined. The physicochemical and microbiological studies have confirmed that the trial drug was free from microbial contamination and heavy metals. All the parameters were found to be within normal or permissible limits. The Pharmacological studies were approved by the Institutional Animal Ethics Committee (IAEC), S.C.R.I, Chennai during 2011. The pharmacological studies, including toxicity (Acute and Sub-acute) and biological activity (Hepatoprotective and Erythropoietic activities) of the test drug ABP were done at Department of Pharmacology, Siddha Central Research Institute, Chennai and C.L.Baid Metha College of Pharmacy, Chennai. The results have shown that the trial drug, ABP is effective and safe in experimental animal models. The Clinical trial on the trial drug ABP was duely approved by the Institutional Ethics Committee (IEC), National Institute of Siddha, Chennai, during the year 2011(IEC approval No.NIS/IEC/2011/1/18). The trial was registered retrospectively in CTRI (CTRI/2014/07/004802). The Clinical trial to evaluate the efficacy and safety of ABP was done at two centres: National Institute of Siddha, Chennai and Siddha Regional Research Institute (under Central Council for Research in Siddha, Chennai), Puducherry. The patients were selected as per the Inclusion, Exclusion criteria and recruited for the trial after receiving their informed consent. The baseline diagnostic parameters were recorded before administering the trial drug ABP as per the protocol.The detailed history with respect to the patient was recorded in Case History Proforma. The Periodical Clinical assessment and assessment of diagnostic parameters was done in regular intervals as per protocol. A total no. of 192 patients were screened out for the trial including pilot study, out of which 92 were recruited with their consent. There were 10 drop-outs due to missed out treatment regimen or withdrawl and a total of 82 patients completed the trial successfully. From this prospective clinical trial, the trial drug was validated for its efficacy and safety in human participants. CONCLUSION: The Physio-chemical analysis of the trial drug Aya Bhringaraja Paanidham has revealed its safety through the results observed found to be with in normal limits as per WHO standards. The results of Toxicity studies (Acute and Sub-Acute) were found safe thereby ensuring the safety of trial drug in experimental animal models. The Pharmacological screening has shown that the trial drug has Erythropoeitic and Hepatoprotective activity in experimental animal models. The Clinical Trial has shown that ABP is effective, safe and cost effective in treating IDA patients. The improvements in the diagnostic features, both clinical and hematological were significant with no adverse alterations in Liver and Renal function tests. This study has validated the traditional formulation ABP for its safety and efficacy in human participants. The trial drug ABP is found to be economical and safe in the management of Veluppu Noi / Anaemia