Showing papers by "Malay K. Kundu published in 2013"

A Neuro-Fuzzy Approach for Medical Image Fusion

Abstract: This paper addresses a novel approach to the multimodal medical image fusion (MIF) problem, employing multiscale geometric analysis of the nonsubsampled contourlet transform and fuzzy-adaptive reduced pulse-coupled neural network (RPCNN). The linking strengths of the RPCNNs' neurons are adaptively set by modeling them as the fuzzy membership values, representing their significance in the corresponding source image. Use of the RPCNN with a less complex structure and having less number of parameters leads to computational efficiency-an important requirement of point-of-care health care technologies. The proposed scheme is free from the common shortcomings of the state-of-the-art MIF techniques: contrast reduction, loss of image fine details, and unwanted image degradations, etc. Subjective and objective evaluations show better performance of this new approach compared to the existing techniques.

Brain Mr Image Classification Using Multiscale Geometric Analysis of Ripplet

Abstract: We propose an automatic and accurate technique for classifying normal and abnormal magnetic resonance (MR) images of human brain. Ripplet transform Type-I (RT), an e-cient multiscale geometric analysis (MGA) tool for digital images, is used to represent the salient features of the brain MR images. The dimensionality of the image representative feature vector is reduced by principal component analysis (PCA). A computationally less expensive support vector machine (SVM), called least square-SVM (LS-SVM) is used to classify the brain MR images. Extensive experiments were carried out to evaluate the performance of the proposed system. Two benchmark MR image datasets and a new larger dataset were used in the experiments, consisting 66, 160 and 255 images, respectively. The generalization capability of the proposed technique is enhanced by 5 £ 5 cross validation procedure. For all the datasets used in the experiments, the proposed system shows high classiflcation accuracies (on an average > 99%). Experimental results and performance comparisons with state-of-the-art techniques, show that the proposed scheme is e-cient in brain MR image classiflcation.

Robust Background Subtraction for Network Surveillance in H.264 Streaming Video

Abstract: The H.264/Advanced Video Coding (AVC) is the industry standard in network surveillance offering the lowest bitrate for a given perceptual quality among any MPEG or proprietary codecs. This paper presents a novel approach for background subtraction in bitstreams encoded in the Baseline profile of H.264/AVC. Temporal statistics of the proposed feature vectors, describing macroblock units in each frame, are used to select potential candidates containing moving objects. From the candidate macroblocks, foreground pixels are determined by comparing the colors of corresponding pixels pair-wise with a background model. The basic contribution of the current work compared to the related approaches is that, it allows each macroblock to have a different quantization parameter, in view of the requirements in variable as well as constant bit-rate applications. Additionally, a low-complexity technique for color comparison is proposed which enables us to obtain pixel-resolution segmentation at a negligible computational cost as compared to those of classical pixel-based approaches. Results showing striking comparison against those of proven state-of-the-art pixel domain algorithms are presented over a diverse set of standardized surveillance sequences.

Distortion free image-in-image communication with implementation in FPGA

Abstract: The proliferation of the digitized media (audio, image and video) introduces a challenging problem for data transmission in the network environment In this paper, a novel, simple and low cost algorithm that serves the purpose of distortion free covert image-in-image communication is proposed Its very large scale integration (VLSI) implementation using field programmable gate array (FPGA) is also developed A binary equivalent message signal is developed first from the combination of the auxiliary gray scale image information and the carrier gray scale image (original) using channel coding and spatial bi-phase modulation scheme The auxiliary image information is then decoded from the distorted/distortion free version of the original image using binary message under certain noise constraint Implementation of the proposed low cost algorithm can be speeded up significantly by hardware realization The developed hardware design allows data transmission at the rate of 4706 Mbits/s at 80 MHz clock frequency

A Ripplet Transform Based Statistical Framework for Natural Color Image Retrieval

Abstract: We present a novel Content Based Image Retrieval (CBIR) scheme for natural color images using Multi-scale Geometric Analysis (MGA) of Ripplet Transform (RT) Type-I in the statistical framework based on Generalized Gaussian Density (GGD) model and Kullback-Leibler Distance (KLD). The system is based on modeling the marginal distributions of RT coefficients by GGD framework and computing the similarity between the model parameters using the KLD. Least Square-Support Vector Machine (LS-SVM) classifier is used to classify the images of the database. Extensive experiments were carried out to evaluate the effectiveness of the proposed system on two image databases consisting 1000 (Simplicity) and 2788 (Oliva) images, respectively. Experimental results and comparisons show that the proposed CBIR system performs efficiently in image retrieval field.

Compact image signature generation: An application in image retrieval

Abstract: In this article, we have proposed a novel Content Based Image Retrieval (CBIR) system, where each image in the database is represented by a compact image signature which is computed using the Nonsubsampled Contourlet Transform (NSCT) and Fuzzy-C-means (FCM) technique. To improve the retrieval accuracy, the proposed system incorporates Least Square Support Vector Machine (LS-SVM) based classifier, Earth Mover's Distance (EMD) and Relevance Feedback Mechanism (RFM). Extensive experiments were carried out to evaluate the effectiveness of the proposed system on SIMPLIcity image database consisting of 1000 images. Experimental results and comparisons show that the proposed CBIR system performs efficiently in image retrieval domain.

Robust Classification of MR Brain Images Based on Multiscale Geometric Analysis

Abstract: The widely used feature representation scheme for magnetic resonance (MR) image classification based on low-frequency subband (LFS) coefficients of wavelet transform (WT) is ineffective in presence of common MR imaging (MRI) artifacts (small rotation, low dynamic range etc.). The directional information present in the high-frequency subbands (HFSs) can be used to improve the performance. Moreover, little attention has been paid to the newly developed multiscale geometric analysis (MGA) tools (curvelet, contourlet, and ripplet etc.) in classifying brain MR images. In this paper, we compare various multiresolution analysis (MRA)/MGA transforms, such as traditional WT, curvelet, contourlet and ripplet, for brain MR image classification. Both the LFS and the high-frequency subbands (HFSs) are used to construct image representative feature vector invariant to common MRI artifacts. The investigations include the effect of different decomposition levels and filters on classification performance. By comparing results, we give the best candidate for classifying brain MR images in presence of common artifacts.

Rough-Fuzzy Clustering and M-Band Wavelet Packet for Text-Graphics Segmentation

Abstract: This paper presents a segmentation method, integrating judiciously the merits of rough-fuzzy computing and multiresolution image analysis technique, for documents having both text and graphics regions. It assumes that the text and non-text regions of a given document are considered to have different textural properties. The M-band wavelet packet is used to extract the scale-space features, which is able to zoom it onto narrow band high frequency components of a signal. A scale-space feature vector is thus derived, taken at different scales for each pixel in an image. Finally, the rough-fuzzy-possibilistic c-means algorithm is used to address the uncertainty problem of document segmentation. The performance of the proposed technique, along with a comparison with related approaches, is demonstrated on a set of real life document images.