Manish Kumar Bajpai

A Graphical Processing Unit–Based Parallel Implementation of Multiplicative Algebraic Reconstruction Technique Algorithm for Limited View Tomography

This article proposes an efficient two-dimensional (2D) pixel-driven multiplicative algebraic reconstruction technique (PdMART) on a general purpose graphical processing unit (GPU), Nvidia graphics card GTX-275. It has been tested on numerical data and also on real data that have been obtained from the micro–computed tomography scanner installed at University of Manchester. We have used real data having 90 projections and 256 rays in each projection to test the algorithm. The real data has been obtained by scanning the graphite core object of size 30 mm × 30 mm. It has been found that GPU can help PdMART to generate the weight matrix for the 256 × 256 pixel grid within a second which is very fast compared to any sequential machine. Experimental results reveal that PdMART on GPU is computationally inexpensive. Preliminary results also indicate much better performance (as compared to popular Fourier methods) for cases of limited-view projection data as is the case for the upcoming laminographic tomography machines.

An efficient algorithm for mass detection and shape analysis of different masses present in digital mammograms

The present study introduces an efficient algorithm for automatic segmentation and detection of mass present in the mammograms. The problem of over and under-segmentation of low-contrast mammographic images has been solved by applying preprocessing on original mammograms. Subtraction operation performed between enhanced and enhanced inverted mammogram significantly highlights the suspicious mass region in mammograms. The segmentation accuracy of suspicious region has been improved by combining wavelet transform and fast fuzzy c-means clustering algorithm. The accuracy of mass segmentation has been quantified by means of Jaccard coefficients. Better sensitivity, specificity, accuracy, and area under the curve (AUC) are observed with support vector machine using radial basis kernel function. The proposed algorithm is validated on Mini-Mammographic Image Analysis Society (MIAS) and Digital Database for Screening Mammography (DDSM) datasets. Highest 91.76% sensitivity, 96.26% specificity, 95.46% accuracy, and 96.29% AUC on DDSM dataset and 94.63% sensitivity, 92.74% specificity, 92.02% accuracy, and 95.33% AUC on MIAS dataset are observed. Also, shape analysis of mass is performed by using moment invariant and Radon transform based features. The best results are obtained with Radon based features and achieved accuracies for round, oval, lobulated, and irregular shape of mass are 100%, 70%, 64%, and 96%, respectively.

A Novel Non-Invasive Method for Extraction of Geometrical and Texture Features of Wood

ABSTRACT Non invasive feature detection in wood based application requires exact discrimination between geometrical edges and texture. It has been found that traditional edge detection algorithms are highly sensitive to noise and texture and produces inferior results with wood. The present work encompasses a micro level reconstruction of Palash and Rosewood by using micro X-rays CT scanner. It also encompasses a new edge detection algorithm using newly constructed Chebyshev polynomial based fractional order differentiator. Transform based method has been used for reconstruction purpose. Newly designed fractional order filter has been applied on these reconstructed images. Chebyshev polynomial based fractional order differentiator has been used for filtering operation. Quadrature Mirror Filter (QMF) concept has been used for design of high pass filter and low pass filter. Preprocessing has been performed by using this filter. Canny edge detection algorithm has been used on this preprocessed image. The algorithm has been tested on two different test cases of wood images a) Palash and b) Rosewood. The effect of relaxation coefficient has also been presented and discussed.

Breast cancer detection in digital mammograms

This paper discusses an approach for automatic detection of abnormalities in the mammograms. Image processing techniques have been applied to accurately segment the suspicious region-of-interest (ROI) prior to abnormality detection. Unsharp masking has been applied for enhancement of the mammogram. Noise removal has been done by using median filtering. Discrete wavelet transform has been applied on filtered image to get the accurate result prior to segmentation. Suspicious ROI has been segmented using the fuzzy-C-means with thresholding technique. Tamura features, shape based features and moment invariants are extracted from the segmented ROI to detect the abnormalities in the mammograms. Proposed algorithm has been validated on the Mini-MIAS data set.

Globally supported radial basis function based collocation method for evolution of level set in mass segmentation using mammograms

Computer-aided detection systems play an important role for the detection of breast abnormalities using mammograms. Global segmentation of mass in mammograms is a complex process due to low contrast mammogram images, irregular shape of mass, speculated margins, and the presence of intensity variations of pixels. This work presents a new approach for mass detection in mammograms, which is based on the variational level set function. Mesh-free based radial basis function (RBF) collocation approach is employed for the evolution of level set function for segmentation of breast as well as suspicious mass region. The mesh-based finite difference method (FDM) is used in literature for evolution of level set function. This work also showcases a comparative study of mesh-free and mesh-based approaches. An anisotropic diffusion filter is employed for enhancement of mammograms. The performance of mass segmentation is analyzed by computing statistical measures. Binarized statistical image features (BSIF) and variants of local binary pattern (LBP) are computed from the segmented suspicious mass regions. These features are given as input to the supervised support vector machine (SVM) classifier to classify suspicious mass region as mass (abnormal) or non-mass (normal) region. Validation of the proposed algorithm is done on sample mammograms taken from publicly available Mini-mammographic image analysis society (MIAS) and Digital Database for Screening Mammography (DDSM) datasets. Combined BSIF features perform better as compared to LBP variants with the performance reported as 97.12% sensitivity, 92.43% specificity, and 98% AUC with 5.12 FP/I on DDSM dataset; and 95.12% sensitivity, 92.41% specificity, and 95% AUC with 4.01FP/I on MIAS dataset.

Reconstruction of original signal from contaminated signal using fractional order differentiator

A discrete time modeled fractional order differentiator has been designed for estimating the fractional order derivative of contaminated signal. The proposed approach uses Chebyshev polynomial based approximation. Riemann-Liouville fractional order derivative definition has been used for design of fractional order Savitzky-Golay differentiator. Proposed algorithm has been validated with different signals. The results show the robustness and sensitivity of the proposed method against noise.

Fast multi-processor multi-GPU based algorithm of tomographic inversion for 3D image reconstruction

Tomographic image reconstruction has a wide variety of applications ranging from engineering applications to medical applications. Algebraic reconstruction methods, used to obtain the solutions of tomographic image reconstruction problems, are very slow in nature. This performance bottleneck has been discussed in detail in the present work. This paper encompasses a parallel (multi-processor based and multi-processor multi-GPU based) single-view coded multiplicative algebraic reconstruction technique. It has been found that parallel implementation of this algorithm helps in removing the performance bottleneck without compromising with quality of reconstruction. It has been also found that if one uses four processors to reconstruct an image of 512 × 512 × 512 volume size, then the multi-processor based algorithm takes 1997 s to perform one swap of 200 projections taken over a span of 360°. The use of four processors leads to an increase in speed of 2.39 in comparison with a single processor. Further, the proposed multi-processor multi-GPU based algorithm takes 186 s to perform the same reconstruction by using four GPUs, resulting in an increase in speed of 25.7 in comparison with a single processor. We are able to process 42 projections per minute by using the multi-processor multi-GPU based algorithm. The algorithm is applicable to online laminographic applications.

High Strain Rate Response of Layered Micro Balloon Filled Aluminum

Recent interest in blast mitigation has given rise to the development of many novel materials and systems. Sandwich materials composed of a soft deformable core sandwiched between two strong face sheets and multilayer structures with alternating deformable and stiff layers have been actively explored for blast mitigation. Understanding the deformation, failure and energy absorption characteristics of such systems is critical for their successful design and application. The present work focuses on understanding the high strain rate response of multilayer structures in which the soft deformable layer is made of micro-balloon filled aluminum. High strain rate experiments are performed using a split Hopkinson pressure bar (SHPB). An ultra high speed camera is used simultaneously to resolve the deformation and failure process in real-time. Experiments are conducted on micro-balloon filled aluminum with different density to understand the effect of the density on the stress–strain characteristics.

An efficient GPU based parallel algorithm for image reconstruction

This paper proposes an efficient GPU based parallel algorithm image reconstruction. It has been implemented on a system having a general purpose graphical processing unit (GPU), Nvidia graphics card GTX-275. Experimental results reveal that an image of size 256 × 256 with 90 projections can be reconstructed in real time with single iteration. The results enable us to use MART algorithm for online applications.

Mesh Free based Variational Level Set Evolution for Breast Region Segmentation and Abnormality Detection using Mammograms

Automatic segmentation of abnormal region is a crucial task in computer-aided detection system using mammograms. In this work, an automatic abnormality detection algorithm using mammographic images is proposed. In the preprocessing step, partial differential equation-based variational level set method is used for breast region extraction. The evolution of the level set method is done by applying mesh-free-based radial basis function (RBF). The limitation of mesh-based approach is removed by using mesh-free-based RBF method. The evolution of variational level set function is also done by mesh-based finite difference method for comparison purpose. Unsharp masking and median filtering is used for mammogram enhancement. Suspicious abnormal regions are segmented by applying fuzzy c-means clustering. Texture features are extracted from the segmented suspicious regions by computing local binary pattern and dominated rotated local binary pattern (DRLBP). Finally, suspicious regions are classified as normal or abnormal regions by means of support vector machine with linear, multilayer perceptron, radial basis, and polynomial kernel function. The algorithm is validated on 322 sample mammograms of mammographic image analysis society (MIAS) and 500 mammograms from digital database for screening mammography (DDSM) datasets. Proficiency of the algorithm is quantified by using sensitivity, specificity, and accuracy. The highest sensitivity, specificity, and accuracy of 93.96%, 95.01%, and 94.48%, respectively, are obtained on MIAS dataset using DRLBP feature with RBF kernel function. Whereas, the highest 92.31% sensitivity, 98.45% specificity, and 96.21% accuracy are achieved on DDSM dataset using DRLBP feature with RBF kernel function.