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S. K. Setua

Bio: S. K. Setua is an academic researcher from University of Calcutta. The author has contributed to research in topics: Node (networking) & Ad hoc On-Demand Distance Vector Routing. The author has an hindex of 7, co-authored 46 publications receiving 177 citations.

Papers
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Journal ArticleDOI
TL;DR: Results of proposed methodology indicates that proposed network is able to perceive ROI in fundus images in 0.0045 s at 25 ms of latency, which is far better than the recent-time and using no handcrafted features.

43 citations

Journal ArticleDOI
TL;DR: The result indicates that not only the proposed method can more effectively enhanced the non-uniform image of retina obtain through thin layer of cataract, but also the resulting image show appropriate brightness and saturation and maintain complete color space information.

40 citations

Journal ArticleDOI
TL;DR: The proposed matched filter and the segmentation technique opens many windows of reliable and faster processing for further image processing steps on retinal fundus images.
Abstract: The appearance and structure of blood vessels in retinal fundus image is a fundamental part of diagnosing different issues related with such as diabetes and hypertension. The proposed blood vessel segmentation in fundus image using Clifford Algebra approach is divided into three steps. Image vectorization as a first step helps to convert the image space into Clifford space. Next step introduces Clifford matched filter as a proposed mask which works for retinal blood vessel extraction. The third and final step of this method is Clifford convolution operation with the help of Clifford convolution. This mask generates edge points along the boundaries of the blood vessels. The edge points are represented as a Grade-0 vector or scalar unit. Discrete edge points along the boundary of blood vessels are the edge pixels instead of continuous edges. The output of this method differs in the representation of vessel tree compare to other existing methods. The output image can be defined as the edge point set. This method achieves blood vessel segmentation accuracy of 94.88% and 92.95% on two publicly available datasets STARE and DRIVE respectively in less than 0.5 s per image. The proposed matched filter and the segmentation technique opens many windows of reliable and faster processing for further image processing steps on retinal fundus images.

27 citations

Proceedings Article
01 Dec 2012
TL;DR: A new parallel cryptography technique is proposed using DNA molecular structure, one-time-pad scheme and DNA hybridization technique which certainly minimizes the time complexity.
Abstract: DNA cryptography is a new promising field in cryptography which emerged with the progress of DNA computing. The concept of massive parallelism and large information density inherent in DNA molecule are exploited for cryptographic purposes. Currently, the main difficulties of DNA cryptography are the requirement of high tech biomolecular laboratory and computational complexity. In this paper, a new parallel cryptography technique is proposed using DNA molecular structure, one-time-pad scheme and DNA hybridization technique which certainly minimizes the time complexity.

26 citations

Proceedings ArticleDOI
01 Feb 2015
TL;DR: Clifford Algebra (CA) and its significance in a color image representation is discussed and the reduction of computational time for image processing is another concern of this paper.
Abstract: Clifford Algebra (CA) and its significance in a color image representation is discussed in this paper. Multivector in 3D space explains the representation of color & grayscale image following RGB color model. The colors are defined by the color blades in multivector. Gray value of a pixel is determined by grayscale vector in multivector form. Rotor operators are applied on different shades of color to obtain a new shade. Every color used in an image is mapped in Clifford color space. The gray values are also closed in it for respective color. The multivector function representing the image follows this color space. It stores an image either in form of color or gray value. The reduction of computational time for image processing is another concern of this paper. The color blade or grayscale vector represents itself as grade-k vector.

10 citations


Cited by
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01 Jan 2002

9,314 citations

Book ChapterDOI
01 Jan 2005

281 citations

Journal ArticleDOI
TL;DR: The Ugi four-component reaction of perfluorinated acids is utilized to establish an exemplary database consisting of 130 commercially available components to create a library of 500,000 molecular keys for encryption and decryption.
Abstract: A convenient and inherently more secure communication channel for encoding messages via specifically designed molecular keys is introduced by combining advanced encryption standard cryptography with molecular steganography. The necessary molecular keys require large structural diversity, thus suggesting the application of multicomponent reactions. Herein, the Ugi four-component reaction of perfluorinated acids is utilized to establish an exemplary database consisting of 130 commercially available components. Considering all permutations, this combinatorial approach can unambiguously provide 500,000 molecular keys in only one synthetic procedure per key. The molecular keys are transferred nondigitally and concealed by either adsorption onto paper, coffee, tea or sugar as well as by dissolution in a perfume or in blood. Re-isolation and purification from these disguises is simplified by the perfluorinated sidechains of the molecular keys. High resolution tandem mass spectrometry can unequivocally determine the molecular structure and thus the identity of the key for a subsequent decryption of an encoded message.

145 citations

Journal ArticleDOI
TL;DR: An overview of the applications of deep learning for ophthalmic diagnosis using retinal fundus images is presented, and various retinal image datasets that can be used for deep learning purposes are described.

118 citations

01 Jan 2009
TL;DR: This chapter contains sections titled: Introduction, An Overview of Service Discovery Protocols, Multiprotocol Service Discovery, Context-Aware Service Discovery for Pervasive Environments, Conclusion, Acknowledgment, Notes, References
Abstract: This chapter contains sections titled: Introduction, An Overview of Service Discovery Protocols, Multiprotocol Service Discovery, Context-Aware Service Discovery for Pervasive Environments, Conclusion, Acknowledgment, Notes, References

112 citations