In their extensive circulation through the cardiovascular circuit, erythrocytes are forced to transform and assume different shapes depending on the varying flow conditions in different blood vessels. The present work aims to visualize various erythrocyte shape transformations by an in vitro microcirculatory model, and to visualize multi-shape erythrocyte deformability. The model used an in-house fabricated and inexpensive disposable micro flow channel to mimic certain in vivo conditions, and a fast frame rate video microscopic system to visualize the shape changes in erythrocytes. Results have shown the multi-shape transformation of erythrocytes as resting discoidal shape, asymmetrically deformed 'hat' and 'bullet-like' shapes, and axially deformed 'slipper-and spindle-like' shapes. Specific erythrocytes demonstrated shape transition and transformation while passing through the observed window. The obtained erythrocyte shaper were thoroughly analyzed based on the deformability index. Findings from the image processing techniques were significant (P<0.001) for the different shapes compared with the resting shape. The simple glass in-house fabricated microfludic channel allowed for multi-shape erythrocyte deformability characterization, many of which manifest in human circulation.
Acute Lymphoblastic Leukemia (ALL) is a blood cancer, which is characterized by an abnormal proliferation of lymphoblasts (a form of white blood cells). It leads to debilitating condition of the patient and necessitates an early therapeutic intervention. For diagnosis of ALL, a pathologist examines bone marrow smear for the count of different types of lymphoblasts to arrive at the diagnosis. The different counts of three different lymphoblasts can affect the type and intensity of the therapy to be used as well as the likely course of the disease. However, bone marrow smear examination and its interpretation is a tedious time consuming process. Therefore, any automation in terms of easing and pacing up the workflow is required. In this paper, we exactly do that by proposing an automatic method for recognition of different types of cells in ALL. The proposed algorithm mimics the pathological and ontological descriptions of bone marrow cells, in identifying abnormalities that lead to ALL. The proposed method uses discriminative shape, colour and texture features, which supposedly contains information for better discrimination of bone marrow cells. Further feature selection techniques, based on mutual information distribution and recursive feature elimination along with Relevance Vector Machines (RVM) are used for effective classification. The results are analyzed on more than 345 cell images. Sensitivity and specificity of above 93% has been achieved.
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