Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

The primary goal of pattern recognition is supervised or unsupervised classification. Among the various frameworks in which pattern recognition has been traditionally formulated, the statistical approach has been most intensively studied and used in practice. More recently, neural network techniques and methods imported from statistical learning theory have been receiving increasing attention. The design of a recognition system requires careful attention to the following issues: definition of pattern classes, sensing environment, pattern representation, feature extraction and selection, cluster analysis, classifier design and learning, selection of training and test samples, and performance evaluation. In spite of almost 50 years of research and development in this field, the general problem of recognizing complex patterns with arbitrary orientation, location, and scale remains unsolved. New and emerging applications, such as data mining, web searching, retrieval of multimedia data, face recognition, and cursive handwriting recognition, require robust and efficient pattern recognition techniques. The objective of this review paper is to summarize and compare some of the well-known methods used in various stages of a pattern recognition system and identify research topics and applications which are at the forefront of this exciting and challenging field.

/pdf/statistical-pattern-recognition-a-review-1kz1b0tte4.pdf

Statistical pattern recognition: a review

An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article.

The Self-Organizing Map

1: Magnetic Particles: Preparation, Properties and Applications: M. Ozaki. 2: Maghemite (gamma-Fe2O3): A Versatile Magnetic Colloidal Material C.J. Serna, M.P. Morales. 3: Dynamics of Adsorption and Oxidation of Organic Molecules on Illuminated Titanium Dioxide Particles Immersed in Water M.A. Blesa, R.J. Candal, S.A. Bilmes. 4: Colloidal Aggregation in Two-Dimensions A. Moncho-Jorda, F. Martinez-Lopez, M.A. Cabrerizo-Vilchez, R. Hidalgo Alvarez, M. Quesada-PMerez. 5: Kinetics of Particle and Protein Adsorption Z. Adamczyk.

Surface and Colloid Science

Hyperspectral imaging (HSI) is an emerging imaging modality for medical applications, especially in disease diagnosis and image-guided surgery. HSI acquires a three-dimensional dataset called hypercube, with two spatial dimensions and one spectral dimension. Spatially resolved spectral imaging obtained by HSI provides diagnostic information about the tissue physiology, morphology, and composition. This review paper presents an overview of the literature on medical hyperspectral imaging technology and its applications. The aim of the survey is threefold: an introduction for those new to the field, an overview for those working in the field, and a reference for those searching for literature on a specific application.

https://www.spiedigitallibrary.org/journals/journal-of-biomedical-optics/volume-19/issue-1/010901/Medical-hyperspectral-imaging-a-review/10.1117/1.JBO.19.1.010901.pdf

Medical hyperspectral imaging: a review

A flat inclined modular photobioreactor (FIMP) for mass cultivation of photoautotrophic microorganisms is described. It consists of flat glass reactors connected in cascade facing the sun with the proper tilt angles to assure maximal exposure to direct beam radiation. The optimal cell density in reference to the length of the reactor light path was evaluated, and the effect of the tilt angle on utilization of both direct beam as well as diffuse sunlight was quantitatively assessed. The mixing mode and extent were also optimized in reference to productivity of biomass. The FIMP proved very successful in supporting continuous cultures of the tested species of photoautotrophs, addressing the major criteria involved in design optimization of photobioreactors: Made of fully transparent glass, inclined toward the sun and endowed with a high surface-to-volume ratio, it combines an optimal light path with a vigorous agitation system. The maximal exposure to the culture to solar irradiance as well as the substantial control of temperature facilitate, under these conditions, a particularly high, extremely light-limited optimal cell density. The integrated effects of these growth conditions resulted in record volumetric and areal output rates of Monodus subterraneus, Anabana siamensis, and Spirulina platensis. (c) 1996 John Wiley & Sons, Inc.

A flat inclined modular photobioreactor for outdoor mass cultivation of photoautotrophs.

An autoadaptive neuro-fuzzy segmentation and edge detection architecture is presented. The system consists of a multilayer perceptron (MLP)-like network that performs image segmentation by adaptive thresholding of the input image using labels automatically pre-selected by a fuzzy clustering technique. The proposed architecture is feedforward, but unlike the conventional MLP the learning is unsupervised. The output status of the network is described as a fuzzy set. Fuzzy entropy is used as a measure of the error of the segmentation system as well as a criterion for determining potential edge pixels. The proposed system is capable to perform automatic multilevel segmentation of images, based solely on information contained by the image itself. No a priori assumptions whatsoever are made about the image (type, features, contents, stochastic model, etc.). Such an "universal" algorithm is most useful for applications that are supposed to work with different (and possibly initially unknown) types of images. The proposed system can be readily employed, "as is," or as a basic building block by a more sophisticated and/or application-specific image segmentation algorithm. By monitoring the fuzzy entropy relaxation process, the system is able to detect edge pixels.

An adaptive neuro-fuzzy system for automatic image segmentation and edge detection

Spectral Matching (SM) is a computationally efficient approach to approximate the solution of pairwise matching problems that are np-hard. In this paper, we present a probabilistic interpretation of spectral matching schemes and derive a novel Probabilistic Matching (PM) scheme that is shown to outperform previous approaches. We show that spectral matching can be interpreted as a Maximum Likelihood (ML) estimate of the assignment probabilities and that the Graduated Assignment (GA) algorithm can be cast as a Maximum a Posteriori (MAP) estimator. Based on this analysis, we derive a ranking scheme for spectral matchings based on their reliability, and propose a novel iterative probabilistic matching algorithm that relaxes some of the implicit assumptions used in prior works. We experimentally show our approaches to outperform previous schemes when applied to exhaustive synthetic tests as well as the analysis of real image sequences.

A Probabilistic Approach to Spectral Graph Matching

Colon cancer is the third leading class of cancer causing increased mortality in developed countries. A polyp is one type of lesion observed in a majority of colon cancer patients. Here, we re- port a microscopic Fourier transform infrared (FTIR) study of normal, adenomatous polyp and malignant cells from biopsies of 24 patients. The goal of our study was to differentiate an adenomatous polyp from a malignant cell using FTIR microspectroscopy and artificial neural network (ANN) analysis. FTIR spectra and biological markers such as phosphate, RNA/DNA derived from spectra, were useful in identifying normal cells from abnormal ones that consisted of adenomatous polyp and malignant cells. However, the biological markers failed to differentiate between adenomatous polyp and malignant cases. By employing a combination of wavelet features and an ANN based clas- sifier, we were able to classify the different cells as normal, adenoma- tous polyp and cancerous in a given tissue sample. The percentage of success of classification was 89%, 81%, and 83% for normal, ad- enomatous polyp, and malignant cells, respectively. A comparison of the method proposed with the pathological method is also discussed.

Diagnostic potential of Fourier-transform infrared microspectroscopy and advanced computational methods in colon cancer patients.

Detection of malignancy at early stages is crucial in cancer prevention and management. Fourier transform infrared (FTIR) spectroscopy has shown promise as a non-invasive method with diagnostic potential in cancer detection. Studies were conducted with formalin-fixed biopsies of melanoma and cervical cancer by FTIR microspectroscopy (FTIR-MSP) to detect common biomarkers, which occurred in both types of cancer distinguishing them from the respective non-malignant tissues. Both types of cancer are diagnosed on skin surfaces. The spectra were analysed for changes in levels of biomolecules such as RNA, DNA, phosphates and carbohydrate (glycogen). Whereas carbohydrate levels showed a good diagnostic potential for detection of cervical cancer, this was not the case for melanoma. However, variation of the RNA/DNA ratio as measured at I(1121)/I(1020) showed similar trends between non-malignant and malignant tissues in both types of cancer. The ratio was higher for malignant tissues in both types of cancer.

Hugo Guterman

Papers

A flat inclined modular photobioreactor for outdoor mass cultivation of photoautotrophs.

An adaptive neuro-fuzzy system for automatic image segmentation and edge detection

A Probabilistic Approach to Spectral Graph Matching

Diagnostic potential of Fourier-transform infrared microspectroscopy and advanced computational methods in colon cancer patients.

Possible common biomarkers from FTIR microspectroscopy of cervical cancer and melanoma