Texture descriptor combining fractal dimension and artificial crawlers

Texture is an important visual attribute used to describe images. There are many methods available for texture analysis. However, they do not capture the details richness of the image surface. In this paper, we propose a new method to describe textures using the artificial crawler model. This model assumes that each agent can interact with the environment and each other. Since this swarm system alone does not achieve a good discrimination, we developed a new method to increase the discriminatory power of artificial crawlers, together with the fractal dimension theory. Here, we estimated the fractal dimension by the Bouligand-Minkowski method due to its precision in quantifying structural properties of images. We validate our method on two texture datasets and the experimental results reveal that our method leads to highly discriminative textural features. The results indicate that our method can be used in different texture applications.Comment: 12 pages 9 figures. Paper in press: Physica A: Statistical Mechanics and its Application

A diagnostic tool for magnesium nutrition in maize based on image analysis of different leaf sections

The nutritional status of maize (Zea mays L.) can be diagnosed by chemical analysis of leaves, which is very slow, or by visual diagnosis of deficiency symptoms, which is dependent on observer experience. The artificial visual system (AVS) is a technology to identify nutritional deficiencies in maize, allowing correction for nutrient supply at earlier development stages in maize. Our objective was to propose methods of artificial vision and pattern recognition to identify the concentration of magnesium (Mg) in maize plants grown in the greenhouse. Magnesium concentrations were 0.0, 0.65, 1.3, and 2.0 mM Mg, with four replications. Leaf scans were collected at V4 (four leaves fully developed), V6 (six leaves fully developed), and V8 (eight leaves fully developed) stages, and these leaves were samples for chemical assays. Such images were processed using AVS methods. Volumetric fractal dimension (VFD), Gabor wavelet (GW), and VFD with canonical analysis (VFDCA) were techniques used by the AVS to extract deficiency characteristics in the leaf images. The increase of Mg in the nutrient solution caused an increase in the Mg concentration in leaves, resulting in typical visual symptoms. The AVS method was able to identify all levels of deficiency, scoring 75.5% of rights in images of the middle section of leaves in the VFDCA method, in color scans of V4 leaves. The AVS was efficient at diagnosing Mg concentrations in leaves of maize during the V4 stage.FAPES

Medical image enhancement

Each image acquired from a medical imaging system is often part of a two-dimensional (2-D) image set whose total presents a three-dimensional (3-D) object for diagnosis. Unfortunately, sometimes these images are of poor quality. These distortions cause an inadequate object-of-interest presentation, which can result in inaccurate image analysis. Blurring is considered a serious problem. Therefore, “deblurring” an image to obtain better quality is an important issue in medical image processing. In our research, the image is initially decomposed. Contrast improvement is achieved by modifying the coefficients obtained from the decomposed image. Small coefficient values represent subtle details and are amplified to improve the visibility of the corresponding details. The stronger image density variations make a major contribution to the overall dynamic range, and have large coefficient values. These values can be reduced without much information loss

Multi-scale Representations for Classification of Protein Crystal Images and Multi-Modal Registration of the Lung

In recent years, multi-resolution techniques have become increasingly popular in the image processing community. New techniques have been developed with applications ranging from edge detection, texture recognition, image registration, multi-resolution features for image classification and more. The central focus of this two-part thesis is the multi-resolution analysis of images. In the first part, we used multi-resolution approaches to help with the classification of a set of protein crystal images. In the second, similar approaches were used to help register a set of 3D image volumes that would otherwise be computationally prohibitive without leveraging multi-resolution techniques. Specifically, the first part of this work proposes a classification framework that is being developed in collaboration with NorthEast Structural Genomics Consoritum (NESG) to assist in the automated screening of protein crystal images. Several groups have previously proposed automated algorithms to expedite such analysis. However, none of the classifiers described in the literature are sufficiently accurate or fast enough to be practical in a structural genomics production pipeline. The second part of this work proposes a 3D image registration algorithm to register regions of emphysema as quantified by densitometry on lung CT with MR lung volumes. The ability to register quantitatively-determined regions of emphysema with perfusion MRI will allow for further exploration of the pathophysiology of Chronic Obstructive Pulmonary Disorder (COPD). The registration method involves the registration of CT volumes at different levels of inspiration (total lung capacity to functional residual capacity [FRC]) followed by another registration between FRC-CT and FRC-MR volume pairs

Automatic segmentation and classification methods using optical coherence tomography angiography (Octa): A review and handbook

Optical coherence tomography angiography (OCTA) is a promising technology for the non-invasive imaging of vasculature. Many studies in literature present automated algorithms to quantify OCTA images, but there is a lack of a review on the most common methods and their comparison considering multiple clinical applications (e.g., ophthalmology and dermatology). Here, we aim to provide readers with a useful review and handbook for automatic segmentation and classification methods using OCTA images, presenting a comparison of techniques found in the literature based on the adopted segmentation or classification method and on the clinical application. Another goal of this study is to provide insight into the direction of research in automated OCTA image analysis, especially in the current era of deep learning