Silvia Ojeda

Performance of Robust RA Estimator for Bidimensional Autoregressive Models

The additive AR-2D model has been successfully related to the modeling of satelital images both optic and of radar of synthetic opening. Having in mind the errors that are produced in the process of captation and quantification of the image, an interesting subject, is the robust estimation of the parameters in this model. Besides the robust methods in image models are also applied in some important image processing situations such as segmentation by texture and image restoration in the presence of outliers. This paper is concerned with the development and performance of the robust RA estimator proposed by Ojeda (1998) for the estimation of parameters in contaminated AR-2D models. Here, we implement this estimator and we show by simulation study that it has a better performance than the classic least square estimator and the robust M and GM estimators in an additive outlier contaminated image model.

A multivariate geostatistical approach for landscape classification from remotely sensed image data

This paper proposes a methodology to address the classification of images that have been acquired from remote sensors. One common problem in classification is the high dimensionality of multivariate characteristics. The methodology we propose consists of reducing the dimensionality of the spectral bands associated with a multispectral satellite image. Such dimensionality reduction is accomplished by the use of the divergence of a modified Mahalanobis distance. Instead of using the covariance matrix of a multivariate spatial process, the codispersion matrix is considered which have some desirable asymptotic properties under very precise conditions. The consistency and asymptotic normality hold for a general class of processes that are a natural extension of the one-dimensional spatial processes for which the asymptotic properties were first established. The results allow the selection of a set of spectral bands to produce the highest value of divergence. Then, a supervised maximum likelihood method using the selected spectral bands is employed for landscape classification. An application with a real LANDSAT image is introduced to explore and visualize how our method works in practice.

Measure of similarity between images based on the codispersion coefficient

We propose to use the codispersion coefficient to define a measure of similarity between images. This coefficient has been widely used in spatial statistics to quantify the association between two spatial processes, and here we explore its capabilities in an image processing context is mathematically simple to compute and possesses good statistical properties. The new measure takes into account the spatial association in a specific direction h between a degraded image and the original unmodified image. Three applications are developed to illustrate the capabilities of our proposal. The defined measure captures the spatial association produced by fitting AR-2D processes with different window sizes. It is able to distinguish the levels of similarity between two images for specific directions in two-dimensional space. Finally, it detects stochastic resonance when an image is transmitted by a nonlinear device.

A new image segmentation algorithm with applications to image inpainting

This article describes a new approach to perform image segmentation. First an image is locally modeled using a spatial autoregressive model for the image intensity. Then the residual autoregressive image is computed. This resulting image possesses interesting texture features. The borders and edges are highlighted, suggesting that our algorithm can be used for border detection. Experimental results with real images are provided to verify how the algorithm works in practice. A robust version of our algorithm is also discussed, to be used when the original image is contaminated with additive outliers. A novel application in the context of image inpainting is also offered.

Transmission of Mal de Río Cuarto virus by first and third-instar nymphs of Delphacodes kuscheli

The ability of first and third-instar Delphacodes kuscheli Fennah (Hemiptera: Delphacidae) nymphs to acquire and transmit Mal de Rio Cuarto virus (MRCV), under controlled conditions, was investigated. First and third-instar nymphs were allowed acquisition feeding separately on infected wheat plants for 48 hours. The insects were then placed in groups of three for serial transmissions to healthy wheat plants, using inoculation periods of 24 hours. Both instars of D. kuscheli were demonstrated to acquire and subsequently transmit the virus. Nevertheless, transmission trials showed highest transmission efficiency and shortest latent period when MRCV was acquired by first-instar nymphs.

A Novel Quality Image Fusion Assessment Based on Maximum Codispersion

In this paper, we present a novel objetive measure for image fusion based on the codispersion quality index, following the structure of Piella’s metric. The measure quantifies the maximum local similarity between two images for many directions using the maximum codispersion quality index. This feature is not commonly assessed by other measures of similarity between images. To vizualize the performance of the maximum codispersion quality index we suggested two graphical tools. The proposed fusion measure is compared to image structural similarity based metrics of the state-of-art. Different experiments performed on several databases show that our metric is consistent with human visual evaluation and can be applied to evaluate different image fusion schemes.

Image Segmentation and Time Series Clustering Based on Spatial and Temporal ARMA Processes

Spatial autoregressive moving average (ARMA) processes have been extensively used in several applications in image/signal processing. In particular, these models have been used for image segmentation, edge detection and image filtering. Image restoration algorithms based on robust estimation of a two-dimensional process have been developed (Kashyap & Eom 1988). Also the two-dimensional autoregressive model has been used to perform unsu‐ pervised texture segmentation (Cariou & Chehdi, 2008). Generalizations of the previous al‐ gorithms using the generalized M estimators to deal with the effect caused by additive contamination was also addressed (Allende et al., 2001). Later on, robust autocovariance (RA) estimators for two dimensional autoregresive (AR-2D) processes were introduced (Oje‐ da, 2002). Several theoretical contributions have been suggested in the literature, including the asymptotic properties of a nearly unstable sequence of stationary spatial autoregressive processes (Baran et al., 2004). Other contributions and applications of spatial ARMA proc‐ esses have been considered in many publications (Basu & Reinsel, 1993, Bustos 2009a, Fran‐ cos & Friendlaner1998, Guyon 1982, Ho 2011, Illig & Truong-Van 2006, Martin1996, Vallejos & Mardesic 2004).

Sialoadenitis por radioyodo. Análisis de factores que influyen en la respuesta al tratamiento médico

OBJECTIVES To assess the incidence of 131I-induced sialadenitis (SD) in patients with differentiated thyroid cancer (DTC), to analyze clinical and other factors related to metabolic radiotherapy that may predict the lack of response to conventional medical therapy (CMT), and to determine the effectiveness of intraductal steroid instillation in patients failing CMT. MATERIAL AND METHODS Fifty-two patients with DTC, 45 females (86.5%) and 7 males (13.5%) with a mean age of 44.21±13.3 years (r=17-74) who received ablation therapy with 131I after total thyroidectomy. Patients with diseases and/or medication causing xerostomia were excluded. Patients underwent salivary gland scintigraphy with 99Tc (10mCi). RESULTS Eighteen patients (34.62%) had SD and received antibiotics, antispasmodics, and oral steroids for 15 days. They were divided into two groups: responders to medical therapy (n=12, age 44.3±14.4 years, 2 men [17%], 10 women [83%], cumulative dose 225±167.1 mCi) and non-responders to medical treatment, who underwent steroid instillation into the Stensens duct (n=6 [33%], 2 men [33%], 4 women [67%], age 50±13.8 years, cumulative dose 138.3±61.7 mCi). Scintigraphy showed damage to the parotid and submaxillary glands. CONCLUSION Incidence of 131I-induced sialadenitis was similar to that reported by other authors. Age, mean cumulative dose of 131I, and involvement of parotid and submaxillary glands did not condition response to CMT; however, male sex was a conditioning factor. Symptom persistence for more than 15 days makes instillation into the Stensens duct advisable. This is an effective and safe method to avoid surgical excision of salivary glands.

A New Algorithm to Represent Texture Images

In recent times the spatial autoregressive models have been extensively used to represent images. In this paper we propose an algorithm to represent and reproduce texture images based on the estimation of spatial autoregressive processes. The image intensity is locally modeled by a first spatial autoregressive model with support in a strongly causal prediction region on the plane. A basic criteria to quantify similarity between two images is used to locally select this region among four different possibilities, corresponding to the four strongly causal regions on the plane. Two global image similarity measures are used to evaluate the performance of our proposal.

Structural Similarity Metrics for Quality Image Fusion Assessment: Algorithms

The wide use of image fusion techniques in different fields such as medical diagnostics, digital camera vision, military and surveillance applications, among others, has motivated the development of various image quality fusion metrics, in order to evaluate them. In this paper, we study and implement the algorithms of non-reference image structural similarity based metrics for fusion assessment: Piella’s metric, Cvejic’s metric, Yang’s metric, and Codispersion Fusion Quality metric. We conduct the comparative experiment of the selected image fusion metrics over four multiresolution image fusion algorithms, performed on different pairs of images used in different applications. Source Code The reviewed source code for this article and documentation for these algorithms are available from the web page of this article1. We used a MATLAB code in the implementation of algorithms.