BIRD: Watershed Based IRis Detection for mobile devices

Communications with a central iris database system using common wireless technologies, such as tablets and smartphones, and iris acquisition out of the field are important functionalities and capabilities of a mobile iris identification device. However, when images are acquired by means of mobile devices under uncontrolled acquisition conditions, noisy images are produced and the effectiveness of the iris recognition system is significantly conditioned. This paper proposes a technique based on watershed transform for iris detection in noisy images captured by mobile devices. The method exploits the information related to limbus to segment the periocular region and merges its score with the iris' one to achieve greater accuracy in the recognition phase

Pupillometry via smartphone for low-resource settings

The photopupillary reflex regulates the pupil reaction to changing light conditions. Being controlled by the autonomic nervous system, it is a proxy for brain trauma and for the conditions of patients in critical care. A prompt evaluation of brain traumas can save lives. With a simple penlight, skilled clinicians can do that, whereas less specialized ones have to resort to a digital pupilometer. However, many low-income countries lack both specialized clinicians and digital pupilometers. This paper presents the early results of our study aiming at designing, prototyping and validating an app for testing the photopupillary reflex via Android, following the European Medical Device Regulation and relevant standards. After a manual validation, the prototype underwent a technical validation against a commercial Infrared pupilometer. As a result, the proposed app performed as well as the manual measurements and better than the commercial solution, with lower errors, higher and significant correlations, and significantly better Bland-Altman plots for all the pupillometry-related measures. The design of this medical device was performed based on our expertise in low-resource settings. This kind of environments imposes more stringent design criteria due to contextual challenges, including the lack of specialized clinicians, funds, spare parts and consumables, poor maintenance, and harsh environmental conditions, which may hinder the safe operationalization of medical devices. This paper provides an overview of how these unique contextual characteristics are cascaded into the design of an app in order to contribute to the Sustainable Development Goal 3 of the World Health Organization: Good health and well-being

A Survey of Iris Recognition System

The uniqueness of iris texture makes it one of the reliable physiological biometric traits compare to the other biometric traits. In this paper, we investigate a different level of fusion approach in iris image. Although, a number of iris recognition methods has been proposed in recent years, however most of them focus on the feature extraction and classification method. Less number of method focuses on the information fusion of iris images. Fusion is believed to produce a better discrimination power in the feature space, thus we conduct an analysis to investigate which fusion level is able to produce the best result for iris recognition system. Experimental analysis using CASIA dataset shows feature level fusion produce 99% recognition accuracy. The verification analysis shows the best result is GAR = 95% at the FRR = 0.1

Detection of uveal melanoma using fuzzy and neural networks classifiers

The use of image processing is increasingly utilized for disease detection. In this article, an algorithm is proposed to detect uveal melanoma (UM) which is a type of intraocular cancer. The proposed method integrates algorithms related to iris segmentation and proposes a novel algorithm for the detection of UM from the approach of fuzzy logic and neural networks. The study case results show 76% correct classification in the fuzzy logic system and 96.04% for the artificial neural networks

Veni Vidi Vici, A Three-Phase Scenario For Parameter Space Analysis in Image Analysis and Visualization

Automatic analysis of the enormous sets of images is a critical task in life sciences. This faces many challenges such as: algorithms are highly parameterized, significant human input is intertwined, and lacking a standard meta-visualization approach. This paper proposes an alternative iterative approach for optimizing input parameters, saving time by minimizing the user involvement, and allowing for understanding the workflow of algorithms and discovering new ones. The main focus is on developing an interactive visualization technique that enables users to analyze the relationships between sampled input parameters and corresponding output. This technique is implemented as a prototype called Veni Vidi Vici, or "I came, I saw, I conquered." This strategy is inspired by the mathematical formulas of numbering computable functions and is developed atop ImageJ, a scientific image processing program. A case study is presented to investigate the proposed framework. Finally, the paper explores some potential future issues in the application of the proposed approach in parameter space analysis in visualization

An image processing pipeline to segment iris for unconstrained cow identification system

One of the most evident costs in cow farming is the identification of the animals. Classic identification processes are labour-intensive, prone to human errors and invasive for the animal. An automated alternative is an animal identification based on unique biometric patterns like iris recognition; in this context, correct segmentation of the region of interest becomes of critical importance. This work introduces a bovine iris segmentation pipeline that processes images taken in the wild, extracting the iris region. The solution deals with images taken with a regular visible-light camera in real scenarios, where reflections in the iris and camera flash introduce a high level of noise that makes the segmentation procedure challenging. Traditional segmentation techniques for the human iris are not applicable given the nature of the bovine eye; at this aim, a dataset composed of catalogued images and manually labelled ground truth data of Aberdeen-Angus has been used for the experiments and made publicly available. The unique ID number for each different animal in the dataset is provided, making it suitable for recognition tasks. Segmentation results have been validated with our dataset showing high reliability: with the most pessimistic metric (i.e. intersection over union), a mean score of 0.8957 has been obtained.Fil: Larregui, Juan Ignacio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación; Argentina. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación; ArgentinaFil: Cazzato, Dario. : University Of Luxembourg; Luxemburgo. Interdisciplinary Centre For Security Reliability And T; LuxemburgoFil: Castro, Silvia Mabel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación; Argentina. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación; Argentin

Computer Vision and Medical Image Processing: a brief survey of application areas

Every day is greater the number of images obtained to characterize the anatomy and functions of the human body, because of this the automation of the medical image processing has become a practice to improve the diagnosis and treatment of certain diseases. In this study the main areas of application of computer vision to the digital processing of medical images are reviewed. It begins with the selection of the three edges with more publications available in Springer, ScienceDirect, Wiley, and IEEE which are: segmentation of organs and lesions, feature extraction in optical images and labelling machine on x-ray images. Over them, latest algorithms, techniques and methods for medical imaging processing are analyzed exposing its main characteristics and ways of use.Sociedad Argentina de Informática e Investigación Operativa (SADIO

Computer Vision and Medical Image Processing: a brief survey of application areas

Every day is greater the number of images obtained to characterize the anatomy and functions of the human body, because of this the automation of the medical image processing has become a practice to improve the diagnosis and treatment of certain diseases. In this study the main areas of application of computer vision to the digital processing of medical images are reviewed. It begins with the selection of the three edges with more publications available in Springer, ScienceDirect, Wiley, and IEEE which are: segmentation of organs and lesions, feature extraction in optical images and labelling machine on x-ray images. Over them, latest algorithms, techniques and methods for medical imaging processing are analyzed exposing its main characteristics and ways of use.Sociedad Argentina de Informática e Investigación Operativa (SADIO