Avijit Dasgupta

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Research in image processing has gained lots of momentum during past two decades. Now-a-days image processing techniques have found their way into computer vision, image compression, image security, medical imaging and more. This paper presents a research on mammography images using wavelet transformation and K – means clustering for cancer tumor mass(More)
Automatic segmentation of retinal blood vessels from fundus images plays an important role in the computer aided diagnosis of retinal diseases. The task of blood vessel segmentation is challenging due to the extreme variations in morphology of the vessels against noisy background. In this paper, we formulate the segmentation task as a multi-label inference(More)
The Demarcation and prediction of the area of the tumor have an important role in medical treatments of malignant tumors. This paper describes an application of Fuzzy set theory in medical image processing, namely brain tumor demarcation. Fuzzy C-Means is proved to be a good and efficient segmentation method. But the main disadvantage of this method is that(More)
This paper describes a rough set approach for color image segmentation that can automatically segment an image to its constituents parts. The aim of the proposed method is to produce an efficient segmentation of color images using intensity information along with neighborhood relationships. The proposed method mainly consists of spatial segmentation; the(More)
We present a physiologically inspired adaptive algorithm for noise removal in an image while preserving significant amount of edge details. The algorithm is motivated by the classical lateral inhibition based receptive field in the visual system as well as the holistic approach of the well known bilateral filter. We propose an adaptive difference of(More)
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