Morphological segmentation analysis and texture-based support vector machines classification on mice liver fibrosis microscopic images

Background To reduce the intensity of the work of doctors, pre-classification work needs to be issued. In this paper, a novel and related liver microscopic image classification analysis method is proposed. Objective For quantitative analysis, segmentation is carried out to extract the quantitative information of special organisms in the image for further diagnosis, lesion localization, learning and treating anatomical abnormalities and computer-guided surgery. Methods in the current work, entropy based features of microscopic fibrosis mice’ liver images were analyzed using fuzzy c-cluster, k-means and watershed algorithms based on distance transformations and gradient. A morphological segmentation based on a local threshold was deployed to determine the fibrosis areas of images. Results the segmented target region using the proposed method achieved high effective microscopy fibrosis images segmenting of mice liver in terms of the running time, dice ratio and precision. The image classification experiments were conducted using Gray Level Co-occurrence Matrix (GLCM). The best classification model derived from the established characteristics was GLCM which performed the highest accuracy of classification using a developed Support Vector Machine (SVM). The training model using 11 features was found to be as accurate when only trained by 8 GLCMs. Conclusion The research illustrated the proposed method is a new feasible research approach for microscopy mice liver image segmentation and classification using intelligent image analysis techniques. It is also reported that the average computational time of the proposed approach was only 2.335 seconds, which outperformed other segmentation algorithms with 0.8125 dice ratio and 0.5253 precision

Advances in Solid State Circuit Technologies

This book brings together contributions from experts in the fields to describe the current status of important topics in solid-state circuit technologies. It consists of 20 chapters which are grouped under the following categories: general information, circuits and devices, materials, and characterization techniques. These chapters have been written by renowned experts in the respective fields making this book valuable to the integrated circuits and materials science communities. It is intended for a diverse readership including electrical engineers and material scientists in the industry and academic institutions. Readers will be able to familiarize themselves with the latest technologies in the various fields

Remote Sensing

This dual conception of remote sensing brought us to the idea of preparing two different books; in addition to the first book which displays recent advances in remote sensing applications, this book is devoted to new techniques for data processing, sensors and platforms. We do not intend this book to cover all aspects of remote sensing techniques and platforms, since it would be an impossible task for a single volume. Instead, we have collected a number of high-quality, original and representative contributions in those areas

Neuro-Fuzzy Based Intelligent Approaches to Nonlinear System Identification and Forecasting

Nearly three decades back nonlinear system identification consisted of several ad-hoc approaches, which were restricted to a very limited class of systems. However, with the advent of the various soft computing methodologies like neural networks and the fuzzy logic combined with optimization techniques, a wider class of systems can be handled at present. Complex systems may be of diverse characteristics and nature. These systems may be linear or nonlinear, continuous or discrete, time varying or time invariant, static or dynamic, short term or long term, central or distributed, predictable or unpredictable, ill or well defined. Neurofuzzy hybrid modelling approaches have been developed as an ideal technique for utilising linguistic values and numerical data. This Thesis is focused on the development of advanced neurofuzzy modelling architectures and their application to real case studies. Three potential requirements have been identified as desirable characteristics for such design: A model needs to have minimum number of rules; a model needs to be generic acting either as Multi-Input-Single-Output (MISO) or Multi-Input-Multi-Output (MIMO) identification model; a model needs to have a versatile nonlinear membership function. Initially, a MIMO Adaptive Fuzzy Logic System (AFLS) model which incorporates a prototype defuzzification scheme, while utilising an efficient, compared to the Takagi–Sugeno–Kang (TSK) based systems, fuzzification layer has been developed for the detection of meat spoilage using Fourier transform infrared (FTIR) spectroscopy. The identification strategy involved not only the classification of beef fillet samples in their respective quality class (i.e. fresh, semi-fresh and spoiled), but also the simultaneous prediction of their associated microbiological population directly from FTIR spectra. In the case of AFLS, the number of memberships for each input variable was directly associated to the number of rules, hence, the “curse of dimensionality” problem was significantly reduced. Results confirmed the advantage of the proposed scheme against Adaptive Neurofuzzy Inference System (ANFIS), Multilayer Perceptron (MLP) and Partial Least Squares (PLS) techniques used in the same case study. In the case of MISO systems, the TSK based structure, has been utilized in many neurofuzzy systems, like ANFIS. At the next stage of research, an Adaptive Fuzzy Inference Neural Network (AFINN) has been developed for the monitoring the spoilage of minced beef utilising multispectral imaging information. This model, which follows the TSK structure, incorporates a clustering pre-processing stage for the definition of fuzzy rules, while its final fuzzy rule base is determined by competitive learning. In this specific case study, AFINN model was also able to predict for the first time in the literature, the beef’s temperature directly from imaging information. Results again proved the superiority of the adopted model. By extending the line of research and adopting specific design concepts from the previous case studies, the Asymmetric Gaussian Fuzzy Inference Neural Network (AGFINN) architecture has been developed. This architecture has been designed based on the above design principles. A clustering preprocessing scheme has been applied to minimise the number of fuzzy rules. AGFINN incorporates features from the AFLS concept, by having the same number of rules as well as fuzzy memberships. In spite of the extensive use of the standard symmetric Gaussian membership functions, AGFINN utilizes an asymmetric function acting as input linguistic node. Since the asymmetric Gaussian membership function’s variability and flexibility are higher than the traditional one, it can partition the input space more effectively. AGFINN can be built either as an MISO or as an MIMO system. In the MISO case, a TSK defuzzification scheme has been implemented, while two different learning algorithms have been implemented. AGFINN has been tested on real datasets related to electricity price forecasting for the ISO New England Power Distribution System. Its performance was compared against a number of alternative models, including ANFIS, AFLS, MLP and Wavelet Neural Network (WNN), and proved to be superior. The concept of asymmetric functions proved to be a valid hypothesis and certainly it can find application to other architectures, such as in Fuzzy Wavelet Neural Network models, by designing a suitable flexible wavelet membership function. AGFINN’s MIMO characteristics also make the proposed architecture suitable for a larger range of applications/problems

Calibration of spectra in presence of non-stationary background using unsupervised physics-informed deep learning

: Calibration is a key part of the development of a diagnostic. Standard approaches require the setting up of dedicated experiments under controlled conditions in order to find the calibration function that allows one to evaluate the desired information from the raw measurements. Sometimes, such controlled experiments are not possible to perform, and alternative approaches are required. Most of them aim at extracting information by looking at the theoretical expectations, requiring a lot of dedicated work and usually involving that the outputs are extremely dependent on some external factors, such as the scientist experience. This work presents a possible methodology to calibrate data or, more generally, to extract the information from the raw measurements by using a new unsupervised physics-informed deep learning methodology. The algorithm allows to automatically process the data and evaluate the searched information without the need for a supervised training by looking at the theoretical expectations. The method is examined in synthetic cases with increasing difficulties to test its potentialities, and it has been found that such an approach can also be used in very complex behaviours, where human-drive results may have huge uncertainties. Moreover, also an experimental test has been performed to validate its capabilities, but also highlight the limits of this method, which, of course, requires particular attention and a good knowledge of the analysed phenomena. The results are extremely interesting, and this methodology is believed to be applied to several cases where classic calibration and supervised approaches are not accessible

Multivariate approach-based system for the automated interpretation of spectra : application to pigments identification through Raman spectroscopy in art analysis

The application of spectroscopic techniques is crucial for art historians and conservators who require knowledge of materials used in works of art (pigments, dyes, binders, additives, ...) in particular instances. In this sense, the knowledge of pigments which were in use on the ancient artists' palettes is fundamental to preserve the art works. In addition, this knowledge is important to determine correct conservation approaches, to study degradation processes or authenticity-related issues. For instance, the proper interpretation of molecular signatures from a vibrational spectroscopy gives valuable information about the materials used by the artists. In this regard, the spectral identification is one of the essential interpretations to be performed, which is generally carried out by visual comparison between the unknown spectra with an appropriate database of reference spectra. This identification approach while being simple and intuitive may turn out a complex task which usually requires an experienced analyst and inevitably introduces an element of subjectivity linked to the intervention of the investigator. Besides, these analyses can be limited due to interferences from other phenomena like noises or admixtures. This task is further complicated when the spectra are to be interpreted by a software system. Hence, the noise impact must be reduced to have an effective identification and a robust strategy for processing multi-component spectra needs to be implemented. Clearly, a fully-automated data processing system for a reliable spectral interpretation is of practical interest. Several automated methodologies were designed, developed and analysed in this Ph.D. Thesis for the purposes of art works analysis through Raman spectroscopy. In this sense, the usage of mathematical morphology together with p-spline fitting demonstrated to be a consistent combination in the application of data enhancement Raman spectra from artistic pigments. Besides, a generalised identification methodology to identify single- and multi- component spectra was developed. This identification method relies on automated spectral matching based on principal component analysis (PCA) and independent components analysis (ICA), being computationally efficient and conceptually simple. Moreover, a supervised classification methodology to automatically distinguish between Raman spectra showing small differences was developed. According to predefined reference training sets, the classification method is able to classify unknown Raman spectra relying on PCA and multiple discriminant analysis (MDA). Both the identification and classification methodologies successfully work using a single spectral observation for the unknown Raman spectra, with no user intervention or previous knowledge of the analysed sample. The designed, developed and analysed automated methodologies for noise filtering and identification and classification of artistic pigments are integrated in a global system for the automated data interpretation of spectra from art works analysis implemented in this Ph.D. Thesis, namely PigmentsLab. This software platform together with the integrated methodologies can play a good auxiliary role in the analysts' endpoint interpretation, providing insight from the raw spectral measurements into pigments. The system implementation provides an easy-to-use software platform and straightforward to update when new spectral data become available. The robust, reliable and consistent results obtained on Raman spectra demonstrated the competitiveness of the implemented data processing solutions. The system has great potential as an accurate and practical method for the automated interpretation of Raman spectra for not only pigment analysis, but essentially for any material group.La aplicación de técnicas espectroscópicas es crucial para los conservadores de arte que requieren el conocimiento de los materiales utilizados en obras de arte (pigmentos, aglutinantes, aditivos, ...) en casos particulares. En este sentido, el conocimiento del uso de los diferentes pigmentos en las paletas de los artistas es fundamental para preservar las obras de arte. Este conocimiento es importante para determinar las estrategias de conservación correctas, para estudiar los procesos de degradación o problemas relacionados con la autenticidad de las obras de arte. Por ejemplo, la interpretación adecuada de las firmas moleculares de una espectroscopia vibracional proporciona información valiosa sobre los materiales utilizados por los artistas. La identificación espectral es una de las interpretaciones esenciales a realizar, y generalmente se lleva a cabo mediante la comparación visual entre los espectros desconocidos con una base de datos adecuada de los espectros de referencia. Esta estrategia de identificación, a pesar de ser sencilla e intuitiva, puede resultar una tarea compleja que requiere generalmente de un analista experimentado e inevitablemente introduce un elemento de subjetividad vinculado a la intervención del investigador. Además, estos análisis pueden verse limitados debido a interferencias de otros fenómenos como ruido o mezclas de pigmentos. Esta tarea se complica aún más cuando los espectros deben ser interpretados por un computador. Por tanto, el impacto del ruido debe ser reducido para tener una identificación eficaz, y se debe implementar una estrategia robusta para el procesado de espectros de múltiples componentes. El desarrollo de un sistema de procesado de datos totalmente automatizado para una interpretación espectral fiable es de evidente interés práctico. Varias metodologías automatizadas han sido diseñadas y desarrolladas en esta tesis doctoral, focalizadas en el análisis de arte mediante espectroscopia Raman. En este sentido, el uso de morfología matemática junto con el ajuste basado en p-splines demostró ser una combinación consistente en la aplicación de mejora de la calidad de espectros Raman de pigmentos artísticos. Además, se ha desarrollado una metodología de identificación generalizada para identificar los espectros Raman compuestos tanto de un solo pigmento como de múltiples pigmentos. Este método de identificación se basa en la búsqueda de coincidencia espectral automatizada basada en el análisis por componentes principales (PCA) y el análisis por componentes independientes (ICA), siendo un método computacionalmente eficiente y conceptualmente simple. Por otra parte, se ha desarrollado una metodología de clasificación supervisada para distinguir entre espectros Raman que muestran pequeñas diferencias entre ellos. A partir de conjuntos de referencia predefinidos de datos de entrenamiento, el método de clasificación es capaz de clasificar los espectros Raman desconocidos mediante PCA y el análisis discriminante múltiple (MDA). Tanto la metodología de identificación como la de clasificación funcionan correctamente utilizando una sola observación espectral para los espectros Raman desconocidos, sin intervención del usuario ni el conocimiento previo de la muestra analizada. Las metodologías automatizadas diseñadas y desarrolladas para el filtrado de ruido y la identificación y clasificación de pigmentos artísticos están integradas en un sistema global para la interpretación automatizada de datos a partir de espectros medidos en obras de arte que ha sido implementado en esta tesis doctoral, llamado PigmentsLab. Esta plataforma software puede representar un buen papel auxiliar en la interpretación de punto final de los analistas, proporcionando valor a partir de las medidas espectrales en bruto de pigmentos artísticos. Los resultados obtenidos en los espectros Raman analizados, siendo robustos y consistentes, demuestran la competitividad de las soluciones de tratamiento de señal implementadas

One-Class Classification: Taxonomy of Study and Review of Techniques

One-class classification (OCC) algorithms aim to build classification models when the negative class is either absent, poorly sampled or not well defined. This unique situation constrains the learning of efficient classifiers by defining class boundary just with the knowledge of positive class. The OCC problem has been considered and applied under many research themes, such as outlier/novelty detection and concept learning. In this paper we present a unified view of the general problem of OCC by presenting a taxonomy of study for OCC problems, which is based on the availability of training data, algorithms used and the application domains applied. We further delve into each of the categories of the proposed taxonomy and present a comprehensive literature review of the OCC algorithms, techniques and methodologies with a focus on their significance, limitations and applications. We conclude our paper by discussing some open research problems in the field of OCC and present our vision for future research.Comment: 24 pages + 11 pages of references, 8 figure

Development of monitoring and control systems for biotechnological processes

The field of biotechnology represents an important research area that has gained increasing success in recent times. Characterized by the involvement of biological organisms in manufacturing processes, its areas of application are broad and include the pharmaceuticals, agri-food, energy, and even waste treatment. The implication of living microorganisms represents the common element in all bioprocesses. Cell cultivations is undoubtedly the key step that requires maintaining environmental conditions in precise and defined ranges, having a significant impact on the process yield and thus on the desired product quality. The apparatus in which this process occurs is the bioreactor. Unfortunately, monitoring and controlling these processes can be a challenging task because of the complexity of the cell growth phenomenon and the limited number of variables can be monitored in real-time. The thesis presented here focuses on the monitoring and control of biotechnological processes, more specifically in the production of bioethanol by fermentation of sugars using yeasts. The study conducted addresses several issues related to the monitoring and control of the bioreactor, in which the fermentation takes place. First, the topic concerning the lack of proper sensors capable of providing online measurements of key variables (biomass, substrate, product) is investigated. For this purpose, nonlinear estimation techniques are analyzed to reconstruct unmeasurable states. In particular, the geometric observer approach is applied to select the best estimation structure and then a comparison with the extended Kalman filter is reported. Both estimators proposed demonstrate good estimation capabilities as input model parameters vary. Guaranteeing the achievement of the desired ethanol composition is the main goal of bioreactor control. To this end, different control strategies, evaluated for three different scenarios, are analzyed. The results show that the MIMO system, together with an estimator for ethanol composition, ensure the compliance with product quality. After analyzing these difficulties through numeric simulations, this research work shifts to testing a specific biotechnological process such as manufacturing bioethanol from brewery’s spent grain (BSG) as renewable waste biomass. Both acid pre-treatment, which is necessary to release sugars, and fermentation are optimized. Results show that a glucose yield of 18.12 per 100 g of dried biomass is obtained when the pre-treatment step is performed under optimized conditions (0.37 M H2SO4, 10% S-L ratio). Regarding the fermentation, T=25°C, pH=4.5, and inoculum volume equal to 12.25% v/v are selected as the best condition, at which an ethanol yield of 82.67% evaluated with respect to theoretical one is obtained. As a final step, the use of Raman spectroscopy combined with chemometric techniques such as Partial Least Square (PLS) analysis is evaluated to develop an online sensor for fermentation process monitoring. The results show that the biomass type involved significantly affects the acquired spectra, making them noisy and difficult to interpret. This represents a nontrivial limitation of the applied methodology, for which more experimental data and more robust statistical techniques could be helpful

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