Feature Selection Method for Iris Recognition Authentication System

Iris-based biometric authentication is gaining importance in recent times. Iris biometric processing however, is a complex process and computationally very expensive. In the overall processing of iris biometric in an iris-based biometric authentication system, feature selection is an important task. In feature selection, we ex-tract iris features, which are ultimately used in matching. Since there is a large number of iris features and computational time increases as the number of features increases, it is therefore a challenge to develop an iris processing system with as few as possible number of features and at the same time without compromising the correctness. In this paper, we address this issue and present an approach to feature Selection Method

Incorporating spatial information for microaneurysm detection in retinal images

The presence of microaneurysms(MAs) in retinal images is a pathognomonic sign of Diabetic Retinopathy (DR). This is one of the leading causes of blindness in the working population worldwide. This paper introduces a novel algorithm that combines information from spatial views of the retina for the purpose of MA detection. Most published research in the literature has addressed the problem of detecting MAs from single retinal images. This work proposes the incorporation of information from two spatial views during the detection process. The algorithm is evaluated using 160 images from 40 patients seen as part of a UK diabetic eye screening programme which contained 207 MAs. An improvement in performance compared to detection from an algorithm that relies on a single image is shown as an increase of 2% ROC score, hence demonstrating the potential of this method

Registration of Brain Images using Fast Walsh Hadamard Transform

A lot of image registration techniques have been developed with great significance for data analysis in medicine, astrophotography, satellite imaging and few other areas. This work proposes a method for medical image registration using Fast Walsh Hadamard transform. This algorithm registers images of the same or different modalities. Each image bit is lengthened in terms of Fast Walsh Hadamard basis functions. Each basis function is a notion of determining various aspects of local structure, e.g., horizontal edge, corner, etc. These coefficients are normalized and used as numerals in a chosen number system which allows one to form a unique number for each type of local structure. The experimental results show that Fast Walsh Hadamard transform accomplished better results than the conventional Walsh transform in the time domain. Also Fast Walsh Hadamard transform is more reliable in medical image registration consuming less time.Comment: 10 pages, 37 figures, 12 table

Unsupervised Detection of Planetary Craters by a Marked Point Process

With the launch of several planetary missions in the last decade, a large amount of planetary images is being acquired. Preferably, automatic and robust processing techniques need to be used for data analysis because of the huge amount of the acquired data. Here, the aim is to achieve a robust and general methodology for crater detection. A novel technique based on a marked point process is proposed. First, the contours in the image are extracted. The object boundaries are modeled as a configuration of an unknown number of random ellipses, i.e., the contour image is considered as a realization of a marked point process. Then, an energy function is defined, containing both an a priori energy and a likelihood term. The global minimum of this function is estimated by using reversible jump Monte-Carlo Markov chain dynamics and a simulated annealing scheme. The main idea behind marked point processes is to model objects within a stochastic framework: Marked point processes represent a very promising current approach in the stochastic image modeling and provide a powerful and methodologically rigorous framework to efficiently map and detect objects and structures in an image with an excellent robustness to noise. The proposed method for crater detection has several feasible applications. One such application area is image registration by matching the extracted features

Determining spaatial patterns in gene expression using in situ hybridization and RNA sequencing data

Determining spatial patterns in gene expression is crucial for understanding physiological function. Image analysis and machine learning play an important role in deriving these patterns from biological data. We first focus on the analysis of single molecule fluorescence in situ hybridization (smFISH) data, obtained from the Human Cell Atlas project. Image registration is an important step in data analysis pipelines which take in image data and output spatially resolved expression of genes. We demonstrate an efficient method to register smFISH images by using a parametric representation of images based on finite rate of innovation sampling, and by optimizing empirical multivariate information measures. We then focus on the analysis of single cell RNA-seq data. When this data is collected, precise spatial information for cells is lost. We compare different approaches to reconstruct the spatial location of cells using RNA-seq data and a reference gene expression atlas. We first compare the predictions obtained by using polynomial regression and a multilayer perceptron regressor. Using polynomial regression we obtain R2 scores of over 0.99 for the prediction of x, y, and z coordinates. Using our multilayer perceptron regressor we obtain R2 scores of 0.96-0.98. We then preselect subsets of informative genes from our original dataset and test the accuracy of our multilayer perceptron regressor using these smaller sized inputs. If we select a subset of 60 genes from our original set of 84 genes, the perceptron can predict location with only a slight loss of precision.Ope

An Orthogonal Learning Differential Evolution Algorithm for Remote Sensing Image Registration

We introduce an area-based method for remote sensing image registration. We use orthogonal learning differential evolution algorithm to optimize the similarity metric between the reference image and the target image. Many local and global methods have been used to achieve the optimal similarity metric in the last few years. Because remote sensing images are usually influenced by large distortions and high noise, local methods will fail in some cases. For this reason, global methods are often required. The orthogonal learning (OL) strategy is efficient when searching in complex problem spaces. In addition, it can discover more useful information via orthogonal experimental design (OED). Differential evolution (DE) is a heuristic algorithm. It has shown to be efficient in solving the remote sensing image registration problem. So orthogonal learning differential evolution algorithm (OLDE) is efficient for many optimization problems. The OLDE method uses the OL strategy to guide the DE algorithm to discover more useful information. Experiments show that the OLDE method is more robust and efficient for registering remote sensing images

Superimposition of eye fundus images for longitudinal analysis from large public health databases

In this paper, a method is presented for superimposition (i.e. registration) of eye fundus images from persons with diabetes screened over many years for diabetic retinopathy. The method is fully automatic and robust to camera changes and colour variations across the images both in space and time. All the stages of the process are designed for longitudinal analysis of cohort public health databases where retinal examinations are made at approximately yearly intervals. The method relies on a model correcting two radial distortions and an affine transformation between pairs of images which is robustly fitted on salient points. Each stage involves linear estimators followed by non-linear optimisation. The model of image warping is also invertible for fast computation. The method has been validated (1) on a simulated montage and (2) on public health databases with 69 patients with high quality images (271 pairs acquired mostly with different types of camera and 268 pairs acquired mostly with the same type of camera) with success rates of 92% and 98%, and five patients (20 pairs) with low quality images with a success rate of 100%. Compared to two state-of-the-art methods, ours gives better results.Comment: This is an author-created, un-copyedited version of an article published in Biomedical Physics \& Engineering Express. IOP Publishing Ltd is not responsible for any errors or omissions in this version of the manuscript or any version derived from it. The Version of Record is available online at https://doi.org/10.1088/2057-1976/aa7d1