Perfusion Quantification using Gaussian Processes
I.K. Andersen, A. Szymkowiak, C. Rasmussen,
L.K. Hansen, J.R. Marstrand, H.B.W. Larsson
Proceedings of the joint meeting of the International
Society of Magnetic Resonance in Medicine and the
European Society of Magnetic Resonance in Medicine
and Biology, 2001
Abstract: The Gaussian Process has not previously
been used for deconvolution with application to quantifying
perfusion form the dynamic R2* measurements. The Gaussian
Process is compared to the Singular Value Decomposition
methods currently used, and is found to be superior.
Automated malignant melanoma diagnosis: Neural network
analysis of chemical alterations presented in Raman
spectra of the cancer
M. Gniadecka, P.A. Philipsen, S. Wessel, O.F. Nielsen,
D.H. Christensen, J. Hercogova, K. Roseen, H.K. Thomsen
, L.K. Hansen, H.C. Wulf.
In preparation, 2001.
Abstract: Malignant melanoma (MM) is the most
aggressive skin cancer. Early detection and removal
of MM is curative, however the specificity and sensitivity
of clinical diagnosis is low and reach only about
80% even in specialised centres. Here, we investigated
whether the chemical changes in the melanoma tissue
can be detected by Raman spectroscopy and used for
tumor diagnosis. Near-infrared Fourier transform (NIR-FT)
Raman spectra were obtained of samples of malignant
melanoma (MM), the lesions from other skin disorders
which can clinically be confused with MM: pigmented
nevi (NV), basal cell carcinoma (BCC), seborrhoeic
keratosis (SK) and normal skin (NOR). The spectra
were analysed visually and by neural network software.
Interestingly, the sensitivity analysis of the spectral
frequencies used by neural network for MM classification
picked up independently frequencies previously used
on visual inspection. We propose that neural network
analysis of NIR-FT Raman spectra could constitute
a novel method for rapid, automated skin cancer diagnosis.
On supervised learning with labeled and unlabeled
Submitted April 2001
Abstract: Based on an analysis of the average
generalization error for
conditional predictive distributions I conclude that
transductive Bayesian learning is generalization optimal
for realizable learning problems with a well-specified
prior. In particular, all available data including
unlabeled data should be used in forming the likelihood.
However, for the unrealizable case the situation is
non-trivial, asymptotic results indicate that unlabeled
data should be used with care, corroborating empirical
results obtained recently by Nigam et al.
Webmining: Learning from the World Wide Web
J. Larsen, L.K. Hansen, A. Szymkowiak, T. Christiansen
and T. Kolenda
To appear in special issue of Computational Statistics
and Data Analysis, 2001 and Proceedings of Nonlinear
Methods and Data Mining 2000, Rome, Italy, Sept. 25-26,
2000, pp. 106-125.
Abstract: These papers study the use of advanced
datamining algorithms for mining webpages. These algorithms
are widely applicable to medical processing systems
and telemedicine and adding flexibilty, effectiveness
and user firendliness. Automated analysis of the world
wide web is a new challenging area relevant in many
applications, e.g., retrieval, navigation and organization
of information, automated information assistants,
and e-commerce. This paper discusses the use of unsupervised
and supervised learning methods for user behavior
modeling and content-based segmentation and classification
of web pages. The modeling is based on independent
component analysis and hierarchical probabilistic
A non-parametric model for separation of histograms
Danish Conference on Pattern Recognition (DANCOMB'00).
Aalborg, Denmark, 31st August - 1st September 2000.
Abstract: The objective of the work is to
separate histograms of classes present in a given
image. It is done without setting any parameters.
The hypothesis is that information about each pixel
of the image can be obtained from its neighbors. The
idea is to use a variable resolution
sensory system as an image data reduction scheme.
In the present work we associate to each pixel the
highest resolution (an optimum window) by detecting
among its neighbors the emergence of a state of greater
order with an increasing function of the fraction
of examples and by maximizing the redundancy in the
data model. Each pixel is associated to a statistic
associated to its optimum window. The output of this
model is a new image with well-separated histograms
of the classes.
Active Learning from the Context Applied to Diffusion
G. Maletti, B. Ersbøll and K. Conradsen
Submitted for the 3rd International Conference on
Modeling and Using Context (CONTEXT'01), 2001.
Contextual Supervised Classifier Based on Normalized
G. Maletti, B. Ersbøll and K. Conradsen
Submitted for the 11th International Conference on
Image Analysis an Processing (ICIAP), 2001.
An Initial Training Set Generation Scheme
G. Maletti, B. Ersbøll, K. Conradsen and J.
11th Scandinavian Conferences on Image Analysis (SCIA
2001). Bergen, Norway, 11th-14th June 2001 (to appear).
Supervised classifiers employ a-priori information
of each determined class. This is usually obtained
by means of training sets interactively generated
or with semi-automatic schemes that still depend on
some user input. The purpose of this work is to generate
an initial statistically valid sample of a class starting
from only one single prototype pixel. This is done
by means of a new mapping model that uses redundant
contextual information. The initial sample generated
is the maximum size
neighborhood centered on the prototype pixel, which
contains pixels that belong to the same class as the
center one. This is defined by a window of optimum
size obtained by minimizing the variance of a learning
variable over the line of maximal redundancy of the
mapping model. The minimum of the function studied
corresponds to the estimation of the point in which
a new class emerges. Additionally, a supervised classifier
was designed and implemented as an application of
the model proposed.
A supervised contextual classification scheme based
G. Maletti and J. Lira
automated region growing algorithm.
5th Iberoamerican Symposium on Pattern Recognition
(SIARP 2000). Lissabon, Portugal, 11th-13th September
Abstract: A supervised classification scheme
to segment optical multi-band images has been developed.
In this classifier, an automated region-growing algorithm
delineates the training sets. This algorithm handles
three parameters: an initial pixel seed, a window
and a threshold for each class. A suitable pixel seed
is manually implanted through visual inspection of
the image classes. The optimum value for the window
and the threshold are obtained from spectral distances.
These distances are calculated from mathematical models
of spectral separabilities. A pixel is incorporated
into a region if a spectral homogeneity criterion
is satisfied in the pixel-centered window
for a given threshold. The homogeneity criterion is
obtained from the models of spectral distances. The
set of pixels forming a region represents a statistically
valid sample of a defined class signaled by the initial
pixel seed. The grown regions constitute therefore
optimum training sets for each class. Comparing the
statistical behavior of a sliding window with that
of each class performs the classification. For classification,
a set of windows is used that provides the best separability
among the classes. The centered pixel of the sliding
window is labeled as belonging to a class if its spectral
distance is a minimum to the class. A series of examples,
employing synthetic images are presented to show the
value of this classifier. The goodness of the segmentation
is evaluated by means of the Kappa coefficient, and
a matrix of distances derived from the model of spectral
On Comparison of Adaptive Regularization Methods
S. Sigurdsson, J. Larsen and L.K. Hansen
Proceedings of the IEEE Workshop on Neural Networks
for Signal Processing X, B. Widrow, L. Guan, K. Paliwa,
T. Adali, J. Larsen, E. Wilson, S. Douglas (eds.),
Piscataway, New Jersey: IEEE. Sydney, Australia, Dec.
11-13, 2000, pp. 221-230.
Abstract: Neural networks are important models
for diagnosis support and has been used for classification
of skin lesions. In this study we address the control
of model complexity and generalization ability which
finds expression in the ubiquitous bias-variance dilemma.
Regularization is a tool for optimizing the model
structure reducing variance at the expense of introducing
extra bias. The overall objective of adaptive regularization
is to tune the amount of regularization ensuring minimal
generalization error. This paper investigates recently
suggested adaptive regularization schemes. Some methods
focus directly on minimizing an estimate of the generalization
error (either algebraic or empirical), whereas others
starts from different criteria, e.g., the Bayesian
evidence. We suggest various algorithm extensions
and performed numerical experiments with linear models.
Detection of Skin Cancer by Classification of Raman
S. Sigurdsson, P.A. Philipsen, L.K. Hansen, J.
Larsen, M. Gniadecka, H.C. Wulf.
In preparation April 2001
Abstract: Skin lesion classification based
on Raman spectroscopy is approached using linear and
neural network non-linear classifiers. Special attention
is devoted to visualization of the learned models.
Resampling experiments allow us to estimate the so-called
learning curve, i.e., the generalization performance
as function of the sample size. We find the typical
generalization cross-over, namely that for small sample
size, a linear model perform better than a more complex
neural net model. As sample size increases eventually
the non-linear model prevails.
The best performance for the present dataset involving
150 cases and 5 classes, is 80% correct, comparable
to expert classifications based on visual inspection.
Hierachical Clustering for Datamining
A. Szymkoviak, J. Larsen, L.K. Hansen
In preparation, May 2001
Abstract: This work present a hierarchical
clustering techniques based on Gaussian mixture models
for datamining relevant for medical diagnosis support
and telemedicine. The method features optimal selection
of subspace dimension, automatic determination of
number of cluster, techniqes for interpretation of
clusters, detection of outliers and the use of labeled
and unlabeled examples. The methods is applied to
Imputating Missing Values in Diary Records of Sun-Exposure
A. Szymkowiak, P.A. Philipsen, J. Larsen, L.K.
Hansen, E. Thieden, H.C. Wulf
In preparation April 2001
Abstract: In a sun-exposure study, questionnaires
concerning sun-habits were collected from 195 subjects
(the group of people involved in the experiment).
Before processing this data, the general problem
with missing values has to be faced. They occur when
some, or even all, the questions have not been answered
in a questionnaire. In this paper, the missing values
of low concentration are investigated. We suggest
two different models for interpolating, namely the
statistical Gaussian model and the non-parametric
K-nearest Neighbor model.