Ispat General Hospital Rourkela

Rourkela, India

Ispat General Hospital Rourkela

Rourkela, India

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Mohapatra S.,National Institute of Technology Rourkela | Patra D.,National Institute of Technology Rourkela | Satpathy S.,Ispat General Hospital Rourkela | Jena R.K.,SCB Medical College | Sethy S.,SCB Medical College
Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization | Year: 2016

Leukaemias are neoplastic proliferations of haemopoietic cells which affect both children and adults and remain one of the leading causes of death around the world. Early diagnosis and classification of such malignant disorders are necessary, and it has always been a challenge in the field of haematology and laboratory medicine. Accurate and authentic diagnosis of leukaemia is essential for the confirmation of the disease, prognostic classification and effective treatment planning. Visual microscopic examination of blood slides is considered as an indispensable diagnostic technique for the screening and classification of leukaemia across continents. However, manual investigation is often slow and limited by subjective assimilation and reduced diagnostic precision. In this study, we investigate the use of image morphometry and pattern recognition techniques for subtyping leukaemic lymphoblasts as per French–American–British classification. Reliable classification results were obtained using the robust segmentation methodology, prominent morphological features and an ensemble of classifiers. To evaluate the performance of the proposed methodology, a comparative study is realised over the available image data-set. The classification rates achieved with the standard classifiers, that is naive Bayesian, K-nearest neighbor, multilayer perceptron, probabilistic neural network and support vector machines, were compared with that obtained using an ensemble of classifiers. It is observed that the classification rate is improved with the use of multiple classifier ensemble and is expected to assist clinicians in making the diagnostic process faster and more accurate. © 2014 Taylor & Francis.


Mohapatra S.,National Institute of Technology Rourkela | Patra D.,National Institute of Technology Rourkela | Satpathi S.,Ispat General Hospital Rourkela
2010 International Conference on Industrial Electronics, Control and Robotics, IECR 2010 | Year: 2010

Acute lymphoblastic leukemia (ALL) is an serious hematological neoplasia of childhood which is characterized by abnormal growth and development of immature white blood cells (lymphoblasts). ALL makes around 80% of childhood leukemia and it mostly occur in the age group of 3-7. The nonspecific nature of the signs and symptoms of ALL often leads to wrong diagnosis. Diagnostic confusion is also posed due to imitation of similar signs by other disorders. Careful microscopic examination of stained blood smear or bone marrow aspirate is the only way to effective diagnosis of leukemia. Techniques such as fluorescence in situ hybridization (FISH), immunophenotyping, cytogenetic analysis and cytochemistry are also employed for specific leukemia detection. The need for automation of leukemia detection arises since the above specific tests are time consuming and costly. Morphological analysis of blood slides are in fluenced by factors such as hematologists experience and tiredness, resulting in non standardized reports. A low cost and efficient solution is to use image analysis for quantitative examination of stained blood microscopic images for leukemia detection. A fuzzy clustering based two stage color segmentation strategy is employed for segregating leukocytes or white blood cells (WBC) from other blood components. Discriminative features i.e. nucleus shape, texture are used for final detection of leukemia. In the present paper two novel shape features i.e., Hausdorff Dimension and contour signature is implemented for classifying a lymphocytic cell nucleus. Support Vector Machine (SVM) is employed for classification. A total of 108 blood smear images were considered for feature extraction and final performance evaluation is validated with the results of a hematologist. © 2010 IEEE.


PubMed | University of Sydney and Ispat General Hospital Rourkela
Type: | Journal: Frontiers in cellular and infection microbiology | Year: 2015

Cerebral malaria is a severe neuropathological complication of Plasmodium falciparum infection. It results in high mortality and post-recovery neuro-cognitive disorders in children, even after appropriate treatment with effective anti-parasitic drugs. While the complete landscape of the pathogenesis of cerebral malaria still remains to be elucidated, numerous innovative approaches have been developed in recent years in order to improve the early detection of this neurological syndrome and, subsequently, the clinical care of affected patients. In this review, we briefly summarize the current understanding of cerebral malaria pathogenesis, compile the array of new biomarkers and tools available for diagnosis and research, and describe the emerging therapeutic approaches to tackle this pathology effectively.

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