Change detection in optical aerial images by a multilayer conditional mixed Markov model

In this paper we propose a probabilistic model for detecting relevant changes in registered aerial image pairs taken with the time differences of several years and in different seasonal conditions. The introduced approach, called the Conditional Mixed Markov model (CXM), is a combination of a mixed Markov model and a conditionally independent random field of signals. The model integrates global intensity statistics with local correlation and contrast features. A global energy optimization process ensures simultaneously optimal local feature selection and smooth, observation-consistent segmentation. Validation is given on real aerial image sets provided by the Hungarian Institute of Geodesy, Cartography and Remote Sensing and Google Earth

A novel neural network approach to cDNA microarray image segmentation

This is the post-print version of the Article. The official published version can be accessed from the link below. Copyright @ 2013 Elsevier.Microarray technology has become a great source of information for biologists to understand the workings of DNA which is one of the most complex codes in nature. Microarray images typically contain several thousands of small spots, each of which represents a different gene in the experiment. One of the key steps in extracting information from a microarray image is the segmentation whose aim is to identify which pixels within an image represent which gene. This task is greatly complicated by noise within the image and a wide degree of variation in the values of the pixels belonging to a typical spot. In the past there have been many methods proposed for the segmentation of microarray image. In this paper, a new method utilizing a series of artificial neural networks, which are based on multi-layer perceptron (MLP) and Kohonen networks, is proposed. The proposed method is applied to a set of real-world cDNA images. Quantitative comparisons between the proposed method and commercial software GenePix(®) are carried out in terms of the peak signal-to-noise ratio (PSNR). This method is shown to not only deliver results comparable and even superior to existing techniques but also have a faster run time.This work was funded in part by the National Natural Science Foundation of China under Grants 61174136 and 61104041, the Natural Science Foundation of Jiangsu Province of China under Grant BK2011598, the International Science and Technology Cooperation Project of China under Grant No. 2011DFA12910, the Engineering and Physical Sciences Research Council (EPSRC) of the U.K. under Grant GR/S27658/01, the Royal Society of the U.K., and the Alexander von Humboldt Foundation of Germany

Analysis of Retinal Image Data to Support Glaucoma Diagnosis

Fundus kamera je široce dostupné zobrazovací zařízení, které umožňuje relativně rychlé a nenákladné vyšetření zadního segmentu oka – sítnice. Z těchto důvodů se mnoho výzkumných pracovišť zaměřuje právě na vývoj automatických metod diagnostiky nemocí sítnice s využitím fundus fotografií. Tato dizertační práce analyzuje současný stav vědeckého poznání v oblasti diagnostiky glaukomu s využitím fundus kamery a navrhuje novou metodiku hodnocení vrstvy nervových vláken (VNV) na sítnici pomocí texturní analýzy. Spolu s touto metodikou je navržena metoda segmentace cévního řečiště sítnice, jakožto další hodnotný příspěvek k současnému stavu řešené problematiky. Segmentace cévního řečiště rovněž slouží jako nezbytný krok předcházející analýzu VNV. Vedle toho práce publikuje novou volně dostupnou databázi snímků sítnice se zlatými standardy pro účely hodnocení automatických metod segmentace cévního řečiště.Fundus camera is widely available imaging device enabling fast and cheap examination of the human retina. Hence, many researchers focus on development of automatic methods towards assessment of various retinal diseases via fundus images. This dissertation summarizes recent state-of-the-art in the field of glaucoma diagnosis using fundus camera and proposes a novel methodology for assessment of the retinal nerve fiber layer (RNFL) via texture analysis. Along with it, a method for the retinal blood vessel segmentation is introduced as an additional valuable contribution to the recent state-of-the-art in the field of retinal image processing. Segmentation of the blood vessels also serves as a necessary step preceding evaluation of the RNFL via the proposed methodology. In addition, a new publicly available high-resolution retinal image database with gold standard data is introduced as a novel opportunity for other researches to evaluate their segmentation algorithms.

FUZZY BINARY PATTERNS FOR UNCERTAINTY-AWARE TEXTURE REPRESENTATION

The Local Binary Pattern (LBP) representation of textures has been proved useful for a wide range of pattern recognition applications, including texture segmentation, face detection, and biomedical image analysis. The interest of the research community in the LBP texture representation gave rise to plenty of LBP and other binary pattern (BP)-based variations. However, noise sensitivity is still a major concern to their applicability on the analysis of real world images. To cope with this problem we propose a generic, uncertainty-aware methodology for the derivation of Fuzzy BP (FBP) texture models. The proposed methodology assumes that a local neighbourhood can be partially characterized by more than one binary patterns due to noise-originated uncertainty in the pixel values. The texture discrimination capability of four representative FBP-based approaches has been evaluated on the basis of comprehensive classification experiments on three reference datasets of natural textures under various types and levels of additive noise. The results reveal that the FBP-based approaches lead to consistent improvement in texture classification as compared with the original BP-based approaches for various degrees of uncertainty. This improved performance is also validated by illustrative unsupervised segmentation experiments on natural scenes

Feature Based Segmentation of Colour Textured Images using Markov Random Field Model

The problem of image segmentation has been investigated with a focus on colored textured image segmentation.Texture is a substantial feature for the analysis of different types of images. Texture segmentation has an assortment of important applications ranging from vision guided autonomous robotics and remote sensing to medical diagnosis and retrieval in large image databases. But the main problem with the textured images is that they contain texture elements of various sizes and in some cases each of which can itself be textured.Thus the texture image segmentation is widely discerned as a difficult and thought-provoking problem.In this thesis an attempt has been made to devise methodologies for automated color textured image segmentation scheme. This problem has been addressed in the literature, still many key open issues remain to be investigated. As an initial step in this direction, this thesis proposes two methods which address the problem of color texture image segmentation through feature extraction approach in partially supervised approach.The feature extraction approaches can be classified into feature based and model based techniques.In feature based technique features are assessed without any model in mind. But in case of model based approach an inherent mathematical model lets eatures to be measured by fitting the model to the texture.The inherent features of the texture are captured in a set of parameters in order to understand the properties generating the texture. Nevertheless, a clear distinction can not be made between the two approaches and hence a combination of approaches from different categories is frequently adopted. In textured image segmentation, image model assumes a significant role and is developed by capturing salient spatial properties of an image. Markov random field (MRF)theory provides a convenient and consistent way to model context dependent entities.In this context a new scheme is proposed using Gaussian MRF model where the segmentation problem is formulated as a pixel labeling problem.The a priori class labels are modeled as Markov random field model and the number of classes is known a priori in partially supervised framework.The image label estimation problem is cast in Bayesian framework using Maximum a Posteriori (MAP)criterion and the MAP estimates of the image labels are obtained using iterated conditional modes (ICM) algorithm. Though the MRF model takes into account the local spatial interactions, it has a limitation in modeling natural scenes of distinct regions. Hence in our formulation, the first scheme takes into account within and between color plane interactions to incorporate spectraland contextual features. Genetic algorithm is employed for the initialization of ICM algorithm to obtain MAP estimates of image labels. The faster convergence property of the ICM algorithm and global convergence property of genetic algorithm are hybridized to obtain segmentation with better accuracy as well as faster convergence

Plant image retrieval using color, shape and texture features

We present a content-based image retrieval system for plant image retrieval, intended especially for the house plant identification problem. A plant image consists of a collection of overlapping leaves and possibly flowers, which makes the problem challenging.We studied the suitability of various well-known color, shape and texture features for this problem, as well as introducing some new texture matching techniques and shape features. Feature extraction is applied after segmenting the plant region from the background using the max-flow min-cut technique. Results on a database of 380 plant images belonging to 78 different types of plants show promise of the proposed new techniques and the overall system: in 55% of the queries, the correct plant image is retrieved among the top-15 results. Furthermore, the accuracy goes up to 73% when a 132-image subset of well-segmented plant images are considered