Rajat Kumar Singh

Local Diagonal Extrema Pattern: A New and Efficient Feature Descriptor for CT Image Retrieval

The medical image retrieval plays an important role in medical diagnosis where a physician can retrieve most similar images from template images against a query image of a particular patient. In this letter, a new and efficient image features descriptor based on the local diagonal extrema pattern (LDEP) is proposed for CT image retrieval. The proposed approach finds the values and indexes of the local diagonal extremas to exploit the relationship among the diagonal neighbors of any center pixel of the image using first-order local diagonal derivatives. The intensity values of the local diagonal extremas are compared with the intensity value of the center pixel to utilize the relationship of central pixel with its neighbors. Finally, the descriptor is formed on the basis of the indexes and comparison of center pixel and local diagonal extremas. The consideration of only diagonal neighbors greatly reduces the dimension of the feature vector which speeds up the image retrieval task and solves the “Curse of dimensionality” problem also. The LDEP is tested for CT image retrieval over Emphysema-CT and NEMA-CT databases and compared with the existing approaches. The superiority in terms of performance and efficiency in terms of speedup of the proposed method are confirmed by the experiments.

Local Wavelet Pattern: A New Feature Descriptor for Image Retrieval in Medical CT Databases

A new image feature description based on the local wavelet pattern (LWP) is proposed in this paper to characterize the medical computer tomography (CT) images for content-based CT image retrieval. In the proposed work, the LWP is derived for each pixel of the CT image by utilizing the relationship of center pixel with the local neighboring information. In contrast to the local binary pattern that only considers the relationship between a center pixel and its neighboring pixels, the presented approach first utilizes the relationship among the neighboring pixels using local wavelet decomposition, and finally considers its relationship with the center pixel. A center pixel transformation scheme is introduced to match the range of center value with the range of local wavelet decomposed values. Moreover, the introduced local wavelet decomposition scheme is centrally symmetric and suitable for CT images. The novelty of this paper lies in the following two ways: 1) encoding local neighboring information with local wavelet decomposition and 2) computing LWP using local wavelet decomposed values and transformed center pixel values. We tested the performance of our method over three CT image databases in terms of the precision and recall. We also compared the proposed LWP descriptor with the other state-of-the-art local image descriptors, and the experimental results suggest that the proposed method outperforms other methods for CT image retrieval.

Local Bit-Plane Decoded Pattern: A Novel Feature Descriptor for Biomedical Image Retrieval

A novel image feature descriptor based on the local bit-plane decoded pattern (LBDP) is introduced for indexing and retrieval of biomedical images in this paper. A local bit-plane transformation scheme is proposed to compute the local bit-plane transformed values for each image pixel from the bit-plane binary contents of its each neighboring pixels. The introduced LBDP is generated by finding a binary pattern using the difference of center pixels intensity value with the local bit-plane transformed values. The efficacy of the LBDP is tested under biomedical image retrieval using average retrieval precision and average retrieval rate. Three benchmark databases Emphysema-CT, NEMA-CT, and Open Access Series of Imaging Studies magnetic resonance imaging are used for the evaluation and comparison of the proposed approach with recent state-of-art methods. The experimental results confirm the discriminative ability and the efficiency of the proposed LBDP for biomedical image indexing and retrieval and prove the outperformance of existing biomedical image retrieval approaches.

WDM-based optical packet switch architectures

We have compared different loop buffer switch architectures in terms of their functionality. Some of these architectures have already been proposed with only their description and operation. The performance evaluation of the switches has been done in terms of packet loss probability for random and bursty traffic. A new architecture has been proposed, which incorporates the good features of the existing architectures.

Multichannel Decoded Local Binary Patterns for Content-Based Image Retrieval

Local binary pattern (LBP) is widely adopted for efficient image feature description and simplicity. To describe the color images, it is required to combine the LBPs from each channel of the image. The traditional way of binary combination is to simply concatenate the LBPs from each channel, but it increases the dimensionality of the pattern. In order to cope with this problem, this paper proposes a novel method for image description with multichannel decoded LBPs. We introduce adder- and decoder-based two schemas for the combination of the LBPs from more than one channel. Image retrieval experiments are performed to observe the effectiveness of the proposed approaches and compared with the existing ways of multichannel techniques. The experiments are performed over 12 benchmark natural scene and color texture image databases, such as Corel-1k, MIT-VisTex, USPTex, Colored Brodatz, and so on. It is observed that the introduced multichannel adder- and decoder-based LBPs significantly improve the retrieval performance over each database and outperform the other multichannel-based approaches in terms of the average retrieval precision and average retrieval rate.

Design Analysis of Optical Loop Memory

This paper describes the fiber optic loop buffer-based switch in which contention is resolved in the time and wavelength domain. In the loop buffer, tunable wavelength converters (TWCs) are placed in place of semiconductor optical amplifiers (SOAs) as in conventional loop buffer-based architectures. The placement of TWCs inside the buffer facilitate simultaneous read/write operation and dynamic re-allocation of wavelengths and improves the switch performance significantly. It is a well known fact that the re-circulating type buffer structure suffers from circulation limit (maximum revolutions that data can take in the buffer) due to the loss and noise accumulation in the switch. This paper presents a mathematical model to obtain a maximum number of allowed circulations of the data in loop buffer-based switch architecture. This model is derived for various configurations (transparent, noisy, and regenerative) of TWC. The detrimental effect of crosstalk and four wave mixing are shown, and the affect of dispersion on the maximum allowed bit rate is discussed. The minimum length of the loop is also evaluated. Finally, the bounded region is shown (bit rate versus number of wavelengths graph) where memory can work efficiently.

Rotation and Illumination Invariant Interleaved Intensity Order-Based Local Descriptor

The region descriptors using local intensity ordering patterns have become more popular recent years for image matching due to its enhanced discriminative ability. However, the dimension of these descriptors increases rapidly with the slight increase in the number of local neighbors under consideration and becomes unreasonable for image matching due to time constraint. In this paper, we reduce the dimension of the descriptor and matching time significantly while keeping up the comparable performance by considering the number of neighboring sample points in an interleaved manner. The proposed interleaved order based local descriptor (IOLD) considers the local neighbors of a pixel as a set of interleaved neighbors and constructs the descriptor over each set separately and finally combines them to produce a single pattern. We extract the local ordering pattern to cope up with the illumination effect in an inherent rotation invariant manner. The novelty lies with using multiple neighboring sets in an interleaved fashion. We also explored the local intensity order pattern in a multisupport-region scenario. Results are compared over three challenging and widely adopted image matching data sets with other prominent descriptors under various image transformations. Results based on experiments suggest that the proposed IOLD descriptor outperforms in terms of both improved matching performance and reduced matching time. We also found that the amount of improvement is significant under complex illumination difference while showing more robustness toward noise.

Identity verification using shape and geometry of human hands

Shape and geometry features are encoded from contour of the hand only.Robust preprocessing is introduced to cope with the noise and disjoint fingers.Hand orientation and finger registration is applied to provide more flexibility.Two level score fusion is adopted to enhance the verification performance.Promising results are obtained over contact and contactless (IITD) datasets. A multimodal biometric system for personal identity verification is proposed using hand shape and hand geometry in this paper. Shape and geometry features are derived with the help of only contour of the hand image for which only one image acquisition device is sufficient. All the processing is done with respect to a stable reference point at wrist line which is more stable as compared to the centroid against the finger rotation and peaks and valleys determination. Two shape based features are extracted by using the distance and orientation of each point of hand contour with respect to the reference point followed by wavelet decomposition to reduce the dimension. Seven distances are used to encode the geometrical information of the hand. Shape and geometry based features are fused at score levels and their performances are evaluated using standard ROC curves between false acceptance rate, true acceptance rate, equal error rate and decidability index. Different similarity measures are used to examine the accuracy of the introduced method. Performance of system is analyzed for shape based (distance and orientation) and geometrical features individually as well as for all possible combinations of feature and score level fusion. The proposed features and fusion methods are studied over two hand image datasets, (1) JUET contact database of 50 subjects having 10 templates each and (2) IITD contactless dataset of 240 subjects with 5 templates each. The proposed method outperforms other approaches with the best 0.31% of EER.

Rotation and scale invariant hybrid image descriptor and retrieval

A rotation and scale invariant hybrid descriptor is proposed for content based image retrieval.Color and textural data are used to construct the descriptor.Color is encoded by quantizing RGB color space into 64 shades.Texture is extracted using 5 rotation invariant structuring element. Accurate image retrieval is required to index and retrieve large number of images from huge databases. In this paper, an efficient approach is presented to encode the color and textural features of images from the local neighborhood of each pixel. The color features are extracted by quantizing the RGB color space into a single channel with reduced number of shades. The texture information is encoded with structuring patterns generated from the locally structured elements chosen as a basis. Color and textural features are fused together to construct the inherently rotation and scale-invariant hybrid image descriptor (RSHD). This fusion is carried out by extracting textural cues over each shade independently. RSHD has been tested on the Corel dataset and experimental results suggest that RSHD outperforms state-of-the-art descriptors. The performance of the RSHD is promising under rotation and scaling. It can also be effectively used under more complex image transformations.

A multi-channel based illumination compensation mechanism for brightness invariant image retrieval

The image retrieval is still challenging to retrieve the most similar images of a given image from a huge database more accurately and robustly. It becomes more challenging for the images having drastic illumination differences. Most of feature descriptor having better retrieval performance degrades in the case of illumination change. To circumvent this problem, we compensated the varying illumination in the image using multi-channel information. We used Red, Green, Blue channel of RGB color space and Intensity channel of HSI color space to remove the intensity change in the image. Finally, we designed an illumination compensated color space to compute the feature descriptor over it. The proposed idea is generic and can be implemented with the most of the feature descriptor. We used some state-of-the-art feature descriptor to show the effectiveness and robustness of proposed color transformation towards uniform and non-uniform illumination change. The experimental results suggest that proposed brightness invariant color transformation can be applied effectively in the retrieval task.