Subrajeet Mohapatra

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Pathological image analysis plays a significant role in effective disease diagnostics. Quantitative microscopy has supplemented clinicians with accurate results for diagnosis of dreaded diseases such as leukemia, hepatitis, AIDS, psoriasis. In this paper we present a texture based approach for automated leukemia detection. Acute lymphocytic leukemia (ALL)(More)
Leukemia is a malignant neoplasm of the blood or bone marrow that affects both children and adults and remains a leading cause of death around the world. Acute lymphoblastic leukemia (ALL) is the most common type of leukemia and is more common among children and young adults. ALL diagnosis through microscopic examination of the peripheral blood and bone(More)
Leukocyte image segmentation acts as the foundation for all automated image based hematological disease recognition systems. Perfection in image segmentation is a necessary condition for improving the diagnostic accuracy in automated cytology. Even though much effort has been put in developing suitable segmentation routines, the problem still remains open(More)
This paper proposes an adaptive threshold selection strategy to detect impulsive noise in images. The proposed method utilizes a simple neural network with statistical characteristics of noisy images. The method is adaptive in the sense that the threshold obtained is adaptable to different type of images and noise conditions. The network tuned for one image(More)
Since noise smoothing and image enhancement are conflicting objectives in most image processing application, so it has to be dealt properly to preserve the quality of the image. The perceptual appearance of an image may be significantly improved by modifying the high frequency components to have better edge and detail information in the image. A simple(More)
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