Computational processing and analysis of ear images

Tese de mestrado. Engenharia Biomédica. Faculdade de Engenharia. Universidade do Porto. 201

Segmentación de imágenes empleando el espacio de escala Gaussiano

La visión por computador trata con el problema de encontrar interpretaciones o descripciones significativas a partir de datos visuales y se pueden pensar tres preguntas que conduzcan a la interpretación significativa de los mismos. ¿Cuál es la información relevante de la imagen? ¿Cómo debe extraerse la información relevante ¿Qué medidas o características pueden obtenerse? Este trabajo pretende responder a la segunda pregunta, así como de identificar desde la imagen qué objetos están en el mundo y donde están en él. Se recurre a la representación en el espacio de escala para el análisis de los datos en diferentes niveles de la imagen y se propone una metodología de segmentación basada en la relación de cada uno de los píxeles con su vecindario. Los espacios de escala son reducciones sucesivas de características de la imagen que permiten identificar las propiedades más significativas de la misma, aplicando un filtro Gaussiano cuyos parámetros son variados a medida que la escala aumenta. Para las pruebas se emplearon imágenes de café y los resultados muestran regiones más completas con respecto a otras técnicas de segmentación / Abstract: The vision by computer deals with the problem to find interpretations or significant descriptions from visual data and can be thought three questions that lead to the significant interpretation of such. Which is the excellent information of the image? How must be extracted the excellent information of the data? What measures or characteristics can be obtained from the extracted information? This work tries to respond to the second question, as well as to identify from the image what objects are in the world and where they are in him. One resorts to the representation in the space of scale for the analysis of the data in different levels from the image and a methodology of segmentation based on the relation of each one of pixels with its neighbourhood sets out. The scale spaces are successive reductions of characteristics of the image that allow to identify the most significant properties of the same one, applying a Gaussian filter whose parameters are varied as the scale increases. For the tests coffee images were used and the results show more complete regions with respect to another techniques of segmentation.Maestrí

Gland Instance Segmentation in Colon Histology Images

This thesis looks at approaches to gland instance segmentation in histology images. The aim is to find suitable local image representations to describe the gland structures in images with benign tissue and those with malignant tissue and subsequently use them for design of accurate, scalable and flexible gland instance segmentation methods. The gland instance segmentation is a clinically important and technically challenging problem as the morphological structure and visual appearance of gland tissue is highly variable and complex. Glands are one of the most common organs in the human body. The glandular features are present in many cancer types and histopathologists use these features to predict tumour grade. Accurate tumour grading is critical for prescribing suitable cancer treatment resulting in improved outcome and survival rate. Different cancer grades are reflected by differences in glands morphology and structure. It is therefore important to accurately segment glands in histology images in order to get a valid prediction of tumour grade. Several segmentation methods, including segmentation with and without pre-classification, have been proposed and investigated as part of the research reported in this thesis. A number of feature spaces, including hand-crafted and deep features, have been investigated and experimentally validated to find a suitable set of image attributes for representation of benign and malignant gland tissue for the segmentation task. Furthermore, an exhaustive experimental examination of different combinations of features and classification methods have been carried out using both qualitative and quantitative assessments, including detection, shape and area fidelity metrics. It has been shown that the proposed hybrid method combining image level classification, to identify images with benign and malignant tissue, and pixel level classification, to perform gland segmentation, achieved the best results. It has been further shown that modelling benign glands using a three-class model, i.e. inside, outside and gland boundary, and malignant tissue using a two-class model is the best combination for achieving accurate and robust gland instance segmentation results. The deep learning features have been shown to overall outperform handcrafted features, however proposed ring-histogram features still performed adequately, particularly for segmentation of benign glands. The adopted transfer-learning model with proposed image augmentation has proven very successful with 100% image classification accuracy on the available test dataset. It has been shown that the modified object- level Boundary Jaccard metric is more suitable for measuring shape similarity than the previously used object-level Hausdorff distance, as it is not sensitive to outliers and could be easily integrated with region- based metrics such as the object-level Dice index, as contrary to the Hausdorff distance it is bounded between 0 and 1. Dissimilar to most of the other reported research, this study provides comprehensive comparative results for gland segmentation, with a large collection of diverse types of image features, including hand-crafted and deep features. The novel contributions include hybrid segmentation model superimposing image and pixel level classification, data augmentation for re-training deep learning models for the proposed image level classification, and the object- level Boundary Jaccard metric adopted for evaluation of instance segmentation methods

Computational strategies for understanding underwater optical image datasets

Thesis: Ph. D. in Mechanical and Oceanographic Engineering, Joint Program in Oceanography/Applied Ocean Science and Engineering (Massachusetts Institute of Technology, Department of Mechanical Engineering; and the Woods Hole Oceanographic Institution), 2013.Cataloged from PDF version of thesis.Includes bibliographical references (pages 117-135).A fundamental problem in autonomous underwater robotics is the high latency between the capture of image data and the time at which operators are able to gain a visual understanding of the survey environment. Typical missions can generate imagery at rates hundreds of times greater than highly compressed images can be transmitted acoustically, delaying that understanding until after the vehicle has been recovered and the data analyzed. While automated classification algorithms can lessen the burden on human annotators after a mission, most are too computationally expensive or lack the robustness to run in situ on a vehicle. Fast algorithms designed for mission-time performance could lessen the latency of understanding by producing low-bandwidth semantic maps of the survey area that can then be telemetered back to operators during a mission. This thesis presents a lightweight framework for processing imagery in real time aboard a robotic vehicle. We begin with a review of pre-processing techniques for correcting illumination and attenuation artifacts in underwater images, presenting our own approach based on multi-sensor fusion and a strong physical model. Next, we construct a novel image pyramid structure that can reduce the complexity necessary to compute features across multiple scales by an order of magnitude and recommend features which are fast to compute and invariant to underwater artifacts. Finally, we implement our framework on real underwater datasets and demonstrate how it can be used to select summary images for the purpose of creating low-bandwidth semantic maps capable of being transmitted acoustically.by Jeffrey W. Kaeli.Ph. D. in Mechanical and Oceanographic Engineerin

A Multiscale Operator For Document Image Binarization

Basically, document image binarization consists on the segmentation of scanned gray level images into text and background, and is a basic preprocessing stage in many image analysis systems. It is essential to threshold the document image reliably in order to extract useful information and make further processing such as character recognition and feature extraction. The main difficulties arise when dealing with poor quality document images, containing nonuniform illumination, shadows and smudge, for example. This paper presents an efficient morphological-based document image binarization technique that is able to cope with these problems. We evaluate the proposed approach for different classes of images, such as historical and machine-printed documents, obtaining promising results.13439(2008), www.flnereader.com, ABBYYBosworth, J., Acton, S., Morphological scale-space in image processing (2003) Digital Signal Processing, 13, pp. 338-367Dorini, L.E.B., Leite, N.J., A scale-space toggle operator for morphological segmentation (2007) 8th International Symposium on Mathematical Morphology, pp. 101-112Dorini, L.E.B., Leite, N.J., Multiscale image representation using scale-space theory (2008) XXXI Congresso National de Matemtica Aplicada e Computational, pp. 130-137Gatos, B., Pratikakis, I., Perantonis, S., Adap-tative degraded image binarization (2006) Pattern Recognition, 39, pp. 317-327Jackway, P.T., Deriche, M., Scale-space properties of the multiscale morphological dilation-erosion (1996) IEEE Transactions on Pattern Analysis and Machine Intelligence, 18, pp. 38-51Maragos, P., Meyer, F., A pde approach to nonlinear image simplification via levelings andrecon-struction filters (2000) International Conference on Image Processing, pp. 938-941Niblack, W., (1986) An Introduction to Digital Image Processing, , Prentice HallOtsu, N., A threshold selection method from grey-level histograms (1979) IEEE Transactions on Systems, Man and Cybernetics, 9 (1), pp. 377-393Parker, J.R., (1996) Algorithms for Image Processing and Computer Vision, , WileySahoo, P., Soltani, S., Wong, A., A survey of thresholding techniques (1988) Comput. Vision, Graphics Image Processing, 41 (2), p. 233260Sauvola, J., Pietikainen, M., Adaptive document image binarization (2000) Pattern Recognition, 33, pp. 225-236Serra, J., Vicent, L., An overview of morphological filtering (1992) Circuits, Systems and Signal Processing, 11 (1), pp. 47-108Sezgin, M., Sankur, B., Survey over image thresholding techniques and quantitative performance evaluation (2004) J. Electron. Imaging, 13, pp. 146-165Trier, O., Jain, A., Goal-directed evaluation of binarization methods (1995) IEEE Trans. Pattern Anal. Mach. Intell, 17, pp. 1191-1201Witkin, A.P., Scale-space filtering: A new approach to multi-scale description (1984) Image Understanding, pp. 79-95. , Able