Shamik Tiwari

Chronic renal insufficiency among Asian Indians with type 2 diabetes: I. Role of RAAS gene polymorphisms

BackgroundRenal failure in diabetes is mediated by multiple pathways. Experimental and clinical evidences suggest that renin-angiotensin-aldosterone system (RAAS) has a crucial role in diabetic kidney disease. A relationship between the RAAS genotypes and chronic renal insufficiency (CRI) among type 2 diabetes subjects has therefore been speculated. We investigated the contribution of selected RAAS gene polymorphisms to CRI among type 2 diabetic Asian Indian subjects.MethodsTwelve single nucleotide polymorphisms (SNPs) from six genes namely-renin (REN), angiotensinogen (ATG), angiotensin converting enzyme I (ACE), angiotensin II type 1 receptor (AT1) and aldosterone synthase (CYP11B2) gene from the RAAS pathway and one from chymase pathway were genotyped using polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) method and tested for their association with diabetic CRI using a case-control approach. Successive cases presenting to study centres with type 2 diabetes of ≥2 years duration and moderate CRI diagnosed by serum creatinine ≥3 mg/dl after exclusion of non-diabetic causes of CRI (n = 196) were compared with diabetes subjects with no evidence of renal disease (n = 225). Logistic regression analysis was carried out to correlate various clinical parameters with genotypes, and to study pair wise interactions between SNPs of different genes.ResultsOf the 12 SNPs genotyped, Glu53Stop in AGT and A>T (-777) in AT1 genes, were monomorphic and not included for further analysis. We observed a highly significant association of Met235Thr SNP in angiotensinogen gene with CRI (O.R. 2.68, 95%CI: 2.01–3.57 for Thr allele, O.R. 2.94, 95%CI: 1.88–4.59 for Thr/Thr genotype and O.R. 2.68, 95%CI: 1.97–3.64 for ACC haplotype). A significant allelic and genotypic association of T>C (-344) SNP in aldosterone synthase gene (O.R. 1.57, 95%CI: 1.16–2.14 and O.R. 1.81, 95%CI: 1.21–2.71 respectively), and genotypic association of GA genotype of G>A (-1903) in chymase gene (O.R. 2.06, 95%CI: 1.34–3.17) were also observed.ConclusionSNPs Met235Thr in angiotensinogen, T>C (-344) in aldosterone synthase, and G>A (-1903) in chymase genes are significantly associated with diabetic chronic renal insufficiency in Indian patients and warrant replication in larger sample sets. Use of such markers for prediction of susceptibility to diabetes specific renal disease in the ethnically Indian population appears promising.

Statistical Moments based Noise Classification using Feed Forward Back Propagation Neural Network

A neural network classification based noise identification method is presented by isolating some representative noise samples, and extracting their statistical features for noise type identification. The isolation of representative noise samples is achieved using prevalent used image filters whereas noise identification is performed using statistical moments features based classification system. The results of the experiments using this method show better identification of noise than those suggested in the recent works. General Terms Image denoising, Pattern recognition.

Wavelet based Multi Class image classification using Neural Network

This paper presents feature extraction and classification of multiclass images by using Haar wavelet transform and back propagation neural network. The wavelet features are extracted from original texture images and corresponding complementary images. The features are made up of different combinations of sub-band images, which offer better discriminating strategy for image classification and enhance the classification rate. General Terms Computer Vision, Pattern Recognition

Flame Detection using Image Processing Techniques

Dynamic textures are common in natural scenes. Examples of dynamic textures in video include fire, smoke, trees in the wind, clouds, sky, ocean waves etc. The fire is characterized using efficient features and detection of the same using a suitable processing. Every pixel is checked for the presence or absence of fire using color features, and periodic behavior in fire regions is also analyzed. In this paper we use combined approach of color detection, motion detection and area dispersion to detect fire in video data. Firstly, the algorithm locates desired color regions in video frames, and then determines the region in the video where there is any movement, and in the last step we calculate the pixel area of the frame. The combination of color, motion and area clues is used to detect fire in the video.

Lung Cancer Detection using Curvelet Transform and Neural Network

the world the common cause of death in humans is lung cancer. It is necessary to detect cancer as early as possible to increase the survival rate. Lung cancer in CT scan images can be classified easily and efficiently using digital image processing techniques. Curvelet transform can extract the features of lung cancer CT scan images proficiently. All extracted feature by curvelet transform are applied to the neural network for training and testing. The performance of proposed work show efficient results. Keywordscancer, Curvelet transforms, Neural Network.

An Edge Detection Method for Grayscale Images based on BP Feedforward Neural Network

The edges provide important visual information since they correspond to major physical and geometrical variations in scene object. Edge detection is a terminology in image processing that refers to algorithms which aim at identifying edges in an image. In this paper a Feedforward Neural Network (FNN) based algorithm is proposed to detect edges in gray scale images. The backpropagation learning algorithm is used to minimize the error. Standard deviation and gradient values are used as training patterns. In the end the network is tested for a number of different kinds of grayscale images. The proposed scheme is compared with Prewitt, Roberts, Sobel, LoG and other neural network based method in which binary training patterns are used. Our method has performed significantly better as compared to other methods. KeywordsDetection, Neural Networks, MATLAB, Backpropagation.

Blur parameters identification for simultaneous defocus and motion blur

Motion blur and defocus blur are common cause of image degradation. Blind restoration of such images demands identification of the accurate point spread function for these blurs. The identification of joint blur parameters in barcode images is considered in this paper using logarithmic power spectrum analysis. First, Radon transform is utilized to identify motion blur angle. Then we estimate the motion blur length and defocus blur radius of the joint blurred image with generalized regression neural network (GRNN). The input of GRNN is the sum of the amplitudes of the normalized logarithmic power spectrum along vertical direction and concentric circles for motion and defocus blurs respectively. This scheme is tested on multiple barcode images with varying parameters of joint blur. We have also analyzed the effect of joint blur when one blur has same, greater or lesser extents to another one. The results of simulation experiments show the high precision of proposed method and reveals that dominance of one blur on another does not affect too much on the applied parameter estimation approach.

Text Segmentation from Images

Texts detection from image or complex colored document is a very challenging problem. Text in images and videos contain useful information. There is a significant need to extract and analyze the text in images on Web documents, for effective indexing, semantic analysis and searching. The extraction of text information is very important because texts contain highlevel semantic information. In this paper, we proposed a hybrid approach of text segmentation using edge and texture feature information .This result can also be used for other image interpretation and analysis.

A Blind Blur Detection Scheme Using Statistical Features of Phase Congruency and Gradient Magnitude

The growing uses of camera-based barcode readers have recently gained a lot of attention. This has boosted interest in no-reference blur detection algorithms. Blur is an undesirable phenomenon which appears as one of the most frequent causes of image degradation. In this paper we present a new no-reference blur detection scheme that is based on the statistical features of phase congruency and gradient magnitude maps. Blur detection is achieved by approximating the functional relationship between these features using a feed forward neural network. Simulation results show that the proposed scheme gives robust blur detection scheme.

ANN Glaucoma Detection using Cup-to-Disk Ratio and Neuroretinal Rim

Glaucoma is a disease in which the intraocular pressure is very high, causing the optic disc to become cupped with eventual everlasting impairment of vision. It is the second leading cause of permanent blindness. It cannot be cured, but its progression can be slowed down by treatment in early stage. Therefore, detecting glaucoma in time is crucial. In this paper glaucoma is classified by extracting two features using retinal fundus images. (i) Cup to Disc Ratio (CDR). (ii) Ratio of Neuroretinal Rim in inferior, superior, temporal and nasal quadrants that is to say ISNT quadrants. Glaucoma frequently damages superior and inferior fibers before temporal and nasal optic nerve fibers and which start decreasing the superior and inferior rims areas and change the order of ISNT rule. Hence, the detection of rim areas in four directions can assist the correct verification of ISNT rule and then improve the correct diagnosis of glaucoma at early stages. In the end, feed forward back propagation neural network is used for classification based on the above two features. The tool used to accomplish the objective is MATLAB R2013a. The average accuracy of the system is around 96%. The method does not rely on trained glaucoma specialists or specialized and costly OCT/HRT machines. Several fundus retinal images containing normal and glaucoma were applied to the proposed method for demonstration.