B. Chandra Mohan

Bio: B. Chandra Mohan is an academic researcher from Bapatla Engineering College. The author has contributed to research in topics: Digital watermarking & Watermark. The author has an hindex of 11, co-authored 21 publications receiving 406 citations. Previous affiliations of B. Chandra Mohan include Jawaharlal Nehru Technological University, Hyderabad.

Multimodal medical image fusion based on Yager’s intuitionistic fuzzy sets

Abstract: The objective of image fusion for medical images is to combine multiple images obtained from various sources into a single image suitable for better diagnosis. Most of the state-of-the-art image fusing technique is based on nonfuzzy sets, and the fused image so obtained lags with complementary information. Intuitionistic fuzzy sets (IFS) are determined to be more suitable for civilian, and medical image processing as more uncertainties are considered compared with fuzzy set theory. In this paper, an algorithm for effectively fusing multimodal medical images is presented. In the proposed method, images are initially converted into Yager’s intuitionistic fuzzy complement images (YIFCIs), and a new objective function called intuitionistic fuzzy entropy (IFE) is employed to obtain the optimum value of the parameter in membership and non-membership functions. Next, the YIFCIs are compared using contrast visibility (CV) to construct a decision map (DM). DM is refined with consistency verification to create a fused image. Simulations on several pairs of multimodal medical images are performed and compared with the existing fusion methods, such as simple average, discrete cosine transform (DCT), redundant wavelet transform (RWT), intuitionistic fuzzy set, fuzzy transform and interval-valued intuitionistic fuzzy set (IVIFS). The superiority of the proposed method is presented and is justified. Fused image quality is also verified with various quality metrics, such as spatial frequency (SF), average gradient (AG), fusion symmetry (FS), edge information preservation (QAB/F), entropy (E) and computation time (CoT).

Hybrid optimization using Ant Colony Optimization and Cuckoo Search in MANET routing

Abstract: Ad hoc networks are a group of mobile nodes with wireless communication adapters which dynamically forms a temporary network. These networks do not require any existing infrastructure or network support. The lack of a central infrastructure imposes challenges, as services ensured by central infrastructure must be ensured by mobile nodes in the new environment. The network operation is decentralized as the network's organization/message delivery is effected by nodes. A Mobile Ad hoc Network (MANET) is functional in either a stand-alone manner or connected to large networks. In this work, a hybrid optimization technique using Ant Colony Optimization (ACO) and Cuckoo Search (CS) is proposed for the optimization of MANET routing. Ad hoc On-demand Distance Vector Routing (AODV) protocol is enhanced using the proposed optimization algorithm. The proposed hybrid algorithm achieves improved performance in terms of average end-to-end delay.

On the use of Schur Decomposition for Copyright Protection of Digital Images

Abstract: This paper presents a robust image watermarking scheme for copyright protection of digital images. In this work, Schur decomposition of host image is explored for watermarking . Watermark image is embedded in the two decompositions of Schur decomposition. Schur decomposition is computationally faster and robust to image attacks than Singular Value Decomposition (SVD). Watermark image is embedded in the D component using scalar quantization. A copy of the watermark is embedded in the columns of U matrix using comparison of the coefficients with respect to the watermark image. Since same watermark is embedded in both U matrix and D matrix the robustness is improved. The proposed algorithm is more secure and robust to various attacks, viz., JPEG2000 compression, JPEG compression, rotation, scaling, cropping, row-column blanking, row-column copying, salt and pepper noise, filtering and gamma correction. Superior experimental results are observed with the proposed algorithm over a recent scheme proposed by Chung et al. in terms of Normalized Cross correlation (NCC) and Peak Signal to Noise Ratio (PSNR).

MANET link performance using Ant Colony Optimization and Particle Swarm Optimization algorithms

Abstract: End-to-end delay and Communication cost are the most important metrics in MANET (Mobile Adhoc Network) routing from source to destination. Recent approaches in Swarm intelligence (SI) technique, a local interaction of many simple agents to meet a global goal, prove that it has more impact on routing in MANETs. Ant Colony Optimization (ACO) algorithm uses mobile agents as ants to discover feasible and best path in a network. ACO helps in finding the paths between two nodes in a network and acts as an input to the Particle Swarm Optimization (PSO) technique, a metaheuristic approach in SI. PSO finds the best solution over the particle's position and velocity with the objective of cost and minimum End-to-end delay. This hybrid algorithm exhibits better performances when compared to ACO approach.

Oblivious Image Watermarking in Discrete Multiwavelet Domain using QIMM

Abstract: This paper proposes a novel oblivious image watermarking technique for the copyright protection and authentication of still images, based on Discrete Multiwavelet Transform (DMWT) and Quantization Index Modulus Modulation (QIMM). The multiwavelet coefficients of an image are quantized using QIMM and the coefficients are modified according to the binary watermark logo. Watermark extraction does not require the original image. Experimental results show that, the proposed watermarking scheme is robust to image compression and rotational attacks. It is superior to the method proposed by Lin et al. [10] in terms of Peak Signal to Noise Ratio (PSNR) and Bit Error Rate (BER).

A Robust Image Watermarking Scheme using Singular Value Decomposition

Abstract: This paper presents a robust image watermarking scheme for multimedia copyright protection. In this work, host image is partitioned into four sub images. Watermark image such as ‘logo’ is embedded in the two of these sub images, in both D (singular and diagonal matrix) and U (left singular and orthogonal matrix) components of Singular Value Decomposition (SVD) of two sub images. Watermark image is embedded in the D component using Dither quantization. A copy of the watermark is embedded in the columns of U matrix using comparison of the coefficients of U matrix with respect to the watermark image. If extraction of watermark from D matrix is not complete, there is a fair amount of probability that it can be extracted from U matrix. The proposed algorithm is more secure and robust to various attacks, viz., JPEG2000 compression, JPEG compression, rotation, scaling, cropping, row-column blanking, row-column copying, salt and pepper noise, filtering and gamma correction. Superior experimental results are observed with the proposed algorithm over a recent scheme proposed by Chung et al. in terms of Bit Error Rate (BER), Normalized Cross correlation (NC) and Peak Signal to Noise Ratio (PSNR).

A Survey on Content Based Image Retrieval Systems

Abstract: Various content-based image retrieval techniques are available for retrieving the require and classify images, we are reviewing them. In our first section, we are tending towards some basics of a particular CBIR system with that we have shown some basic features of any image, these are like shape, texture, color and shown different techniques to calculate them. In the next section, we have shown different distance measuring techniques used for similarity measurement of any image and also discussed indexing techniques. Finally conclusion and future scope is discussed.

A study of multiplicative watermark detection in the contourlet domain using alpha-stable distributions.

Abstract: In the past decade, several schemes for digital image watermarking have been proposed to protect the copyright of an image document or to provide proof of ownership in some identifiable fashion. This paper proposes a novel multiplicative watermarking scheme in the contourlet domain. The effectiveness of a watermark detector depends highly on the modeling of the transform-domain coefficients. In view of this, we first investigate the modeling of the contourlet coefficients by the alpha-stable distributions. It is shown that the univariate alpha-stable distribution fits the empirical data more accurately than the formerly used distributions, such as the generalized Gaussian and Laplacian, do. We also show that the bivariate alpha-stable distribution can capture the across scale dependencies of the contourlet coefficients. Motivated by the modeling results, a blind watermark detector in the contourlet domain is designed by using the univariate and bivariate alpha-stable distributions. It is shown that the detectors based on both of these distributions provide higher detection rates than that based on the generalized Gaussian distribution does. However, a watermark detector designed based on the alpha-stable distribution with a value of its parameter α other than 1 or 2 is computationally expensive because of the lack of a closed-form expression for the distribution in this case. Therefore, a watermark detector is designed based on the bivariate Cauchy member of the alpha-stable family for which α = 1 . The resulting design yields a significantly reduced-complexity detector and provides a performance that is much superior to that of the GG detector and very close to that of the detector corresponding to the best-fit alpha-stable distribution. The robustness of the proposed bivariate Cauchy detector against various kinds of attacks, such as noise, filtering, and compression, is studied and shown to be superior to that of the generalized Gaussian detector.