Spatially adaptive Bayesian image reconstruction through locally-modulated Markov random field models

The use of Markov random field (MRF) models has proven to be a fruitful approach in a wide range of image processing applications. It allows local texture information to be incorporated in a systematic and unified way and allows statistical inference theory to be applied giving rise to novel output summaries and enhanced image interpretation. A great advantage of such low-level approaches is that they lead to flexible models, which can be applied to a wide range of imaging problems without the need for significant modification. This paper proposes and explores the use of conditional MRF models for situations where multiple images are to be processed simultaneously, or where only a single image is to be reconstructed and a sequential approach is taken. Although the coupling of image intensity values is a special case of our approach, the main extension over previous proposals is to allow the direct coupling of other properties, such as smoothness or texture. This is achieved using a local modulating function which adjusts the influence of global smoothing without the need for a fully inhomogeneous prior model. Several modulating functions are considered and a detailed simulation study, motivated by remote sensing applications in archaeological geophysics, of conditional reconstruction is presented. The results demonstrate that a substantial improvement in the quality of the image reconstruction, in terms of errors and residuals, can be achieved using this approach, especially at locations with rapid changes in the underlying intensity

Avaliaçao de descritores de textura para segmentaçao de imagens

Orientador: Hélio PedriniInclui apendiceDissertaçao (mestrado) - Universidade Federal do Paraná, Setor de Ciencias Exatas, Programa de Pós-Graduaçao em Informática. Defesa: Curitiba, 2005Inclui bibliografi

Ultrasound imaging system combined with multi-modality image analysis algorithms to monitor changes in anatomical structures

This dissertation concerns the development and validation of an ultrasound imaging system and novel image analysis algorithms applicable to multiple imaging modalities. The ultrasound imaging system will include a framework for 3D volume reconstruction of freehand ultrasound: a mechanism to register the 3D volumes across time and subjects, as well as with other imaging modalities, and a playback mechanism to view image slices concurrently from different acquisitions that correspond to the same anatomical region. The novel image analysis algorithms include a noise reduction method that clusters pixels into homogenous patches using a directed graph of edges between neighboring pixels, a segmentation method that creates a hierarchical graph structure using statistical analysis and a voting system to determine the similarity between homogeneous patches given their neighborhood, and finally, a hybrid atlas-based registration method that makes use of intensity corrections induced at anatomical landmarks to regulate deformable registration. The combination of the ultrasound imaging system and the image analysis algorithms will provide the ability to monitor nerve regeneration in patients undergoing regenerative, repair or transplant strategies in a sequential, non-invasive manner, including visualization of registered real-time and pre-acquired data, thus enabling preventive and therapeutic strategies for nerve regeneration in Composite Tissue Allotransplantation (CTA). The registration algorithm is also applied to MR images of the brain to obtain reliable and efficient segmentation of the hippocampus, which is a prominent structure in the study of diseases of the elderly such as vascular dementia, Alzheimer’s, and late life depression. Experimental results on 2D and 3D images, including simulated and real images, with illustrations visualizing the intermediate outcomes and the final results are presented.

Segmentaçao de imagens baseada em dependencia espacial utilizando campo aleatório de Markov associado com características de texturas

Orientador: Hélio PedriniDissertaçao (mestrado) - Universidade Federal do Paraná, Setor de Ciencias Exatas, Programa de Pós-Graduaçao em Informática. Defesa: Curitiba, 2005Inclui bibliografiaResumo: Uma etapa crítica presente no processo de análise de imagens é a segmentação, responsável por obter informações de alto n'nível sobre as regiões ou objetos contidos na imagem, de modo a facilitar sua interpretação. Contudo, a segmentação ainda é um dos maiores desafios na área de análise de imagens, particularmente quando não se utiliza informações previamente adquiridas sobre a imagem a ser segmentada. Os métodos convencionais de segmentação desconsideram a dependência espacial entre as regiões, o que pode gerar resultados impróprios. Técnicas que consideram a dependência espacial entre as regiões da imagem têm recebido crescente atenção da comunidade científica, pois apresentam uma maior precisão nos resultados obtidos. Embora avanços significativos tenham sido alcançados na segmentação de texturas e de imagens coloridas separadamente, a combinação dessas duas propriedades é considerada como um problema bem mais complexo. Devido a importância dessa etapa no processo de análise de imagens e ao fato de não existirem soluções definitivas para o problema, este trabalho propõe o desenvolvimento de um novo método de segmentação aplicado a imagens texturizadas monocromáticas e coloridas. O método utiliza a formulação Bayesiana para associar a dependência espacial modelada por um campo aleatório de Markov com características de texturas. A segmentação final é obtida por meio da aplicação de t'cênicas de relaxação para minimizar uma função de energia definida a partir da referida associação. Experimentos são efetuados visando avaliar os métodos de análise de texturas, bem como validar a metodologia proposta.Abstract: A critical stage present in the image analysis process is the segmentation, responsible for obtaining high level information about regions or objects in the image, in order to facilitate its interpretation. However, the segmentation is still one of the greatest challenges in the image analysis area, particularly when it does not use information previously acquired on the image to be segmented. Conventional segmentation methods do not consider the spatial dependence between the regions, which can generate improper results. Techniques considering the spatial dependence between the image regions have received increasing attention from the scientific community, because they present a major precision in the obtained results. Although significant advances have been reached in the segmentation of textures and colored images separately, the combination of these two properties is considered a more complex problem. Due to the importance of this stage in the image analysis process and to the fact that does not exist definitive solutions to the problem, this work considers the development of a new segmentation method applied to gray scale and color texture images. The method uses the Bayesian formulation to associate the spatial dependence modeled by a Markov random field with texture features. The final segmentation is obtained by the application of relaxation techniques to minimize an energy function defined by such association. Experiments are performed to evaluate the texture analysis methods, as well as validating the proposal method

Texture feature extraction and classification : a comparative study between traditional methods and deep learning : a thesis presented in partial fulfilment of the requirements for the degree of Master of Information Science in Computer Sciences at Massey University, Auckland, New Zealand

Figure 3.1 (=Kaehler & Bradski, 2017 Fig 1-4, p. 9) was removed for copyright reasons.Image classification has always been a core problem of computer vision. With the development of deep learning, it also provides a good solution for us to solve the problem of image feature extraction in image classification. In this thesis we used machine learning and convolutional neural network to study texture feature extraction and classification problems. We implemented a pipeline within the sklearn framework that utilized Local Binary Pattern (LBP) and Haralick as our feature descriptor and various classifiers (namely KNearest Neighbors, Linear Discriminant Analysis, Support Vector Machines, Multilayer Perceptron, Gaussian Naive Bayes, Random Forest, AdaBoost, Logistic Regression and Decision Tree) to evaluate the performance on some popular texture datasets (Brodatz dataset, four extended Outex datasets and VisTex dataset). We also employed Linear Discriminant Analysis as our dimension reduction schema to observe the changes in classification accuracy. We also took advantage of Keras with TensorFlow backend framework and built a pipeline that uses ImageNet-trained convolutional neural network models to train and analyze classifier, extract image feature information and make predictions on test dataset samples. This allowed us to compare the results between traditional methods and CNN based methods. It was found that the classification accuracy has been greatly improved with the CNN based method