Histopathological image analysis : a review

Over the past decade, dramatic increases in computational power and improvement in image analysis algorithms have allowed the development of powerful computer-assisted analytical approaches to radiological data. With the recent advent of whole slide digital scanners, tissue histopathology slides can now be digitized and stored in digital image form. Consequently, digitized tissue histopathology has now become amenable to the application of computerized image analysis and machine learning techniques. Analogous to the role of computer-assisted diagnosis (CAD) algorithms in medical imaging to complement the opinion of a radiologist, CAD algorithms have begun to be developed for disease detection, diagnosis, and prognosis prediction to complement the opinion of the pathologist. In this paper, we review the recent state of the art CAD technology for digitized histopathology. This paper also briefly describes the development and application of novel image analysis technology for a few specific histopathology related problems being pursued in the United States and Europe

Digital Image-Based Frameworks for Monitoring and Controlling of Particulate Systems

Particulate processes have been widely involved in various industries and most products in the chemical industry today are manufactured as particulates. Previous research and practise illustrate that the final product quality can be influenced by particle properties such as size and shape which are related to operating conditions. Online characterization of these particles is an important step for maintaining desired product quality in particulate processes. Image-based characterization method for the purpose of monitoring and control particulate processes is very promising and attractive. The development of a digital image-based framework, in the context of this research, can be envisioned in two parts. One is performing image analysis and designing advanced algorithms for segmentation and texture analysis. The other is formulating and implementing modern predictive tools to establish the correlations between the texture features and the particle characteristics. According to the extent of touching and overlapping between particles in images, two image analysis methods were developed and tested. For slight touching problems, image segmentation algorithms were developed by introducing Wavelet Transform de-noising and Fuzzy C-means Clustering detecting the touching regions, and by adopting the intensity and geometry characteristics of touching areas. Since individual particles can be identified through image segmentation, particle number, particle equivalent diameter, and size distribution were used as the features. For severe touching and overlapping problems, texture analysis was carried out through the estimation of wavelet energy signature and fractal dimension based on wavelet decomposition on the objects. Predictive models for monitoring and control for particulate processes were formulated and implemented. Building on the feature extraction properties of the wavelet decomposition, a projection technique such as principal component analysis (PCA) was used to detect off-specification conditions which generate particle mean size deviates the target value. Furthermore, linear and nonlinear predictive models based on partial least squares (PLS) and artificial neural networks (ANN) were formulated, implemented and tested on an experimental facility to predict particle characteristics (mean size and standard deviation) from the image texture analysis

Automatic Detection and Characterization of Pathological Fluid Regions in Optical Coherence Tomography Images

Programa Oficial de Doutoramento en Computación. 5009V01[Abstract] Intraretinal fluid accumulation is both the common symptom and culprit of the main causes of blindness in developed countries: Age-related Macular Degeneration and Diabetic Macular Edema. For its diagnosis, experts of the domain employ Optical Coherence Tomography images (OCT), providing non-invasive cross-sectional representations of the retinal structures. However, like any medical imaging modality, OCT is influenced by multiple factors that impact its quality and subsequent interpretation. Coupled with the subjectiveness of the human experts, these factors can significantly affect the diagnostic process, treatment and quality of life for the affected individuals (particularly in these pathologies where early detection is crucial). To address these challenges, Computer-Aided Diagnosis (CAD) methodologies are developed, offering a layer of abstraction of the information present in the images. Still, in the particular scenario of these pathological fluid accumulations, the development of these methodologies is specially difficult due to their diffuse nature without defined boundaries. In this thesis, we proposed different CAD methodologies with the objective of helping expert clinicians to better detect and understand these pathologies. Furthermore, we expand the developed methodologies to other medical imaging modalities and conditions, such as macular neovascularizations in OCT Angiographies and COVID-19 diagnosis through the analysis of lung chest radiographs.[Resumen] La acumulación de líquido intrarretiniano es tanto síntoma común como culpable de las principales causas de ceguera en los países desarrollados: la degeneración macular asociada a la edad y el edema macular diabético. Para su diagnóstico, los expertos en el campo emplean imágenes de Tomografía de Coherencia Óptica (OCT), que proporcionan representaciones transversales no invasivas de las estructuras retinianas. Sin embargo, al igual que cualquier modalidad de imagen médica, OCT se ve influenciado por múltiples factores que afectan a su calidad y posterior interpretación. Junto con la subjetividad de los expertos humanos, estos factores pueden afectar significativamente el proceso diagnóstico, tratamiento y calidad de vida de las personas afectadas (particularmente en estas patologías donde una detección temprana es crucial). Para abordar estos desafíos, se desarrollan metodologías de diagnóstico asistido por ordenador (CAD), que ofrecen una capa de abstracción de la información presente en las imágenes. Sin embargo, en el escenario particular de estas acumulaciones patológicas de fluido, el desarrollo de estas metodologías es especialmente difícil debido a su naturaleza difusa, sin bordes definidos. En esta tesis doctoral proponemos diferentes metodologías CAD con el objetivo de ayudar a las personas expertas del dominio a detectar y comprender mejor estas patologías. Además, expandimos las metodologías desarrolladas a otras modalidades de imagen médica y afecciones, como al análisis de neovascularizaciones maculares en Angiografía OCT y al diagnóstico de COVID-19 mediante radiografías torácicas.[Resumo] A acumulación de líquido intrarretiniano é tanto o síntoma común como culpable das principais causas de cegueira nos países desenvolvidos: a dexeneración macular asociada á idade e o edema macular diabético. Para o seu diagnóstico, os expertos no campo empregan imaxes de tomografía de coherencia óptica (OCT), que proporcionan representacións transversais non invasivas das estruturas retinianas. Non obstante, ao igual que calquera modalidade de imaxe médica, a OCT vese influenciada por múltiples factores que afectan a s´ua calidade e a súa posterior interpretación. Xunto coa subxectividade dos expertos humanos, estes factores poden afectar significativamente ao proceso diagn´ostico, ao tratamento e á calidade de vida das persoas afectadas (particularmente nestas patoloxías onde unha detección precoz é crucial). Para abordar estes desafíos, desenvólvense metodoloxías de diagnóstico asistido por ordenador (CAD), que ofrecen unha capa de abstracción da información presente nas imaxes. Non obstante, no escenario particular das acumulacións patolóxicas de líquido, o desenvolvemento destas metodoloxías é especialmente difícil debido a súa natureza difusa, sen bordes definidos. Nesta tese de doutoramento propoñemos diferentes metodoloxías de CAD co obxectivo de axudar ás persoas expertas do campo a detectar e comprender mellor estas patoloxías. Ademais, expandimos as metodoloxías desenvoltas a outras modalidades de imaxe médica e patoloxías, como a an´alise de neovascularizacións maculares en Anxiografía OCT e ao diagnóstico da COVID-19 mediante a análise de radiografías torácicas

Two and three dimensional segmentation of multimodal imagery

The role of segmentation in the realms of image understanding/analysis, computer vision, pattern recognition, remote sensing and medical imaging in recent years has been significantly augmented due to accelerated scientific advances made in the acquisition of image data. This low-level analysis protocol is critical to numerous applications, with the primary goal of expediting and improving the effectiveness of subsequent high-level operations by providing a condensed and pertinent representation of image information. In this research, we propose a novel unsupervised segmentation framework for facilitating meaningful segregation of 2-D/3-D image data across multiple modalities (color, remote-sensing and biomedical imaging) into non-overlapping partitions using several spatial-spectral attributes. Initially, our framework exploits the information obtained from detecting edges inherent in the data. To this effect, by using a vector gradient detection technique, pixels without edges are grouped and individually labeled to partition some initial portion of the input image content. Pixels that contain higher gradient densities are included by the dynamic generation of segments as the algorithm progresses to generate an initial region map. Subsequently, texture modeling is performed and the obtained gradient, texture and intensity information along with the aforementioned initial partition map are used to perform a multivariate refinement procedure, to fuse groups with similar characteristics yielding the final output segmentation. Experimental results obtained in comparison to published/state-of the-art segmentation techniques for color as well as multi/hyperspectral imagery, demonstrate the advantages of the proposed method. Furthermore, for the purpose of achieving improved computational efficiency we propose an extension of the aforestated methodology in a multi-resolution framework, demonstrated on color images. Finally, this research also encompasses a 3-D extension of the aforementioned algorithm demonstrated on medical (Magnetic Resonance Imaging / Computed Tomography) volumes