6,938 research outputs found
Segmentation of articular cartilage and early osteoarthritis based on the fuzzy soft thresholding approach driven by modified evolutionary ABC optimization and local statistical aggregation
Articular cartilage assessment, with the aim of the cartilage loss identification, is a crucial task for the clinical practice of orthopedics. Conventional software (SW) instruments allow for just a visualization of the knee structure, without post processing, offering objective cartilage modeling. In this paper, we propose the multiregional segmentation method, having ambitions to bring a mathematical model reflecting the physiological cartilage morphological structure and spots, corresponding with the early cartilage loss, which is poorly recognizable by the naked eye from magnetic resonance imaging (MRI). The proposed segmentation model is composed from two pixel's classification parts. Firstly, the image histogram is decomposed by using a sequence of the triangular fuzzy membership functions, when their localization is driven by the modified artificial bee colony (ABC) optimization algorithm, utilizing a random sequence of considered solutions based on the real cartilage features. In the second part of the segmentation model, the original pixel's membership in a respective segmentation class may be modified by using the local statistical aggregation, taking into account the spatial relationships regarding adjacent pixels. By this way, the image noise and artefacts, which are commonly presented in the MR images, may be identified and eliminated. This fact makes the model robust and sensitive with regards to distorting signals. We analyzed the proposed model on the 2D spatial MR image records. We show different MR clinical cases for the articular cartilage segmentation, with identification of the cartilage loss. In the final part of the analysis, we compared our model performance against the selected conventional methods in application on the MR image records being corrupted by additive image noise.Web of Science117art. no. 86
Bandwidth selection for kernel estimation in mixed multi-dimensional spaces
Kernel estimation techniques, such as mean shift, suffer from one major
drawback: the kernel bandwidth selection. The bandwidth can be fixed for all
the data set or can vary at each points. Automatic bandwidth selection becomes
a real challenge in case of multidimensional heterogeneous features. This paper
presents a solution to this problem. It is an extension of \cite{Comaniciu03a}
which was based on the fundamental property of normal distributions regarding
the bias of the normalized density gradient. The selection is done iteratively
for each type of features, by looking for the stability of local bandwidth
estimates across a predefined range of bandwidths. A pseudo balloon mean shift
filtering and partitioning are introduced. The validity of the method is
demonstrated in the context of color image segmentation based on a
5-dimensional space
Local Variation as a Statistical Hypothesis Test
The goal of image oversegmentation is to divide an image into several pieces,
each of which should ideally be part of an object. One of the simplest and yet
most effective oversegmentation algorithms is known as local variation (LV)
(Felzenszwalb and Huttenlocher 2004). In this work, we study this algorithm and
show that algorithms similar to LV can be devised by applying different
statistical models and decisions, thus providing further theoretical
justification and a well-founded explanation for the unexpected high
performance of the LV approach. Some of these algorithms are based on
statistics of natural images and on a hypothesis testing decision; we denote
these algorithms probabilistic local variation (pLV). The best pLV algorithm,
which relies on censored estimation, presents state-of-the-art results while
keeping the same computational complexity of the LV algorithm
Color image segmentation using a self-initializing EM algorithm
This paper presents a new method based on the Expectation-Maximization (EM) algorithm that we apply for color image segmentation. Since this algorithm partitions the data based on an initial set of mixtures, the color segmentation provided by the EM algorithm is highly dependent on the starting condition (initialization stage). Usually the initialization procedure selects the color seeds randomly and often this procedure forces the EM algorithm to converge to numerous local minima and produce inappropriate results. In this paper we propose a simple and yet effective solution to initialize the EM algorithm with relevant color seeds. The resulting self initialised EM algorithm has been included in the development of an adaptive image segmentation scheme that has been applied to a large number of color images. The experimental data indicates that the refined initialization procedure leads to improved color segmentation
Measuring Blood Glucose Concentrations in Photometric Glucometers Requiring Very Small Sample Volumes
Glucometers present an important self-monitoring tool for diabetes patients
and therefore must exhibit high accu- racy as well as good usability features.
Based on an invasive, photometric measurement principle that drastically
reduces the volume of the blood sample needed from the patient, we present a
framework that is capable of dealing with small blood samples, while
maintaining the required accuracy. The framework consists of two major parts:
1) image segmentation; and 2) convergence detection. Step 1) is based on
iterative mode-seeking methods to estimate the intensity value of the region of
interest. We present several variations of these methods and give theoretical
proofs of their convergence. Our approach is able to deal with changes in the
number and position of clusters without any prior knowledge. Furthermore, we
propose a method based on sparse approximation to decrease the computational
load, while maintaining accuracy. Step 2) is achieved by employing temporal
tracking and prediction, herewith decreasing the measurement time, and, thus,
improving usability. Our framework is validated on several real data sets with
different characteristics. We show that we are able to estimate the underlying
glucose concentration from much smaller blood samples than is currently
state-of-the- art with sufficient accuracy according to the most recent ISO
standards and reduce measurement time significantly compared to
state-of-the-art methods
Non-Parametric Probabilistic Image Segmentation
We propose a simple probabilistic generative model for
image segmentation. Like other probabilistic algorithms
(such as EM on a Mixture of Gaussians) the proposed model
is principled, provides both hard and probabilistic cluster
assignments, as well as the ability to naturally incorporate
prior knowledge. While previous probabilistic approaches
are restricted to parametric models of clusters (e.g., Gaussians)
we eliminate this limitation. The suggested approach
does not make heavy assumptions on the shape of the clusters
and can thus handle complex structures. Our experiments
show that the suggested approach outperforms previous
work on a variety of image segmentation tasks
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