17-03-2014, 08:05 PM
Abstract— In this paper, we describe a pixel based approach
for automated segmentation of tumor components from MR
images. Sparse coding with data-adapted dictionaries has been
successfully employed in several image recovery and vision
problems. Since it is trivial to obtain sparse codes for pixel
values, we propose to consider their non-linear similarities to
perform kernel sparse coding in a high dimensional feature space.
We develop the kernel K-lines clustering procedure for inferring
kernel dictionaries and use the kernel sparse codes to determine
if a pixel belongs to a tumorous region. By incorporating spatial
locality information of the pixels, contiguous tumor regions can
be efficiently identified. A low complexity segmentation approach,
which allows the user to initialize the tumor region, is also
presented. Results show that both of the proposed approaches
lead to accurate tumor identification with a low false positive
rate, when compared to manual segmentation by an expert.
Index Terms—MRI, tumor segmentation, sparse representations,
kernel methods
I. INTRODUCTION
A robust method to automatically segment a medical image
into its constituent heterogeneous regions can be an extremely
valuable tool for clinical diagnosis and disease modeling.
Given a reasonably large data set, performing manual segmentation
is not a practical approach. Brain tumor detection
and segmentation have been of interest to researchers over
the recent years and currently, there exists no comprehensive
algorithm built and adopted in the clinical setting [1]. Although
patient scans can be obtained using different imaging modalities,
Magnetic Resonance Imaging (MRI) has been commonly
adopted for brain imaging over other modalities because of
its non-invasive and non-ionizing nature, and ability for direct
multi-plane imaging.
Tumors may be malignant or benign as determined by a
biopsy, and are known to affect brain symmetry and cause
damages to the surrounding brain tissues. Automated tumor
segmentation approaches are often challenged by the variability
in size, shape and location of the tumor, the high
degree of similarity in the pixel intensities between normal
and abnormal brain tissue regions, and the intensity variations
of identical tissues across volumes. As a result, unsupervised
thresholding techniques have not been very successful in
accurate tumor segmentation [2]. Furthermore, approaches that
incorporate prior knowledge of the normal brain from atlases
require accurate non-rigid registration [3], [4], and hence
generating adequate segmentation results potentially calls for
user-intervention and/or a patient specific training system. In
addition, these methods require elaborate pre-processing and
they tend to over-estimate the tumor volume.
Approaches for tumor segmentation can be either regionbased
or pixel based. The active contours method [5] is a
widely adopted region-based approach that is usually combined
with a level-set evolution for convergence to a region
of interest [6]. However, it is sensitive to the contour initialization,
and has a high computational cost due to its iterative
nature. Model-based approaches [7] employ geometric priors
to extend the Expectation Maximization (EM) algorithm to
augment statistical classification. In relatively homogeneous
cases such as low grade gliomas, the outlier detection framework
proposed by Prastawa et al. [2], [8] was shown to
perform well.
Pixel based approaches such as Fuzzy C-Means (FCM)
using neighborhood labels [9], Conditional Random Fields
[10], Bayesian model-aware affinities extending the SWA
algorithm [1], and the more recent graph-based techniques
combined with Cellular-Automata (CA) algorithm [11] have
also achieved some success in tumor segmentation. However,
processing issues with respect to contour initialization, noise
reduction, intensity standardization, cluster selection, spatial
registration, and the need for accurate manual seed-selection
leaves substantial room for improvement. In addition, building
a robust automated approach that does not require user intervention
is very important, particularly for processing large
datasets.
for automated segmentation of tumor components from MR
images. Sparse coding with data-adapted dictionaries has been
successfully employed in several image recovery and vision
problems. Since it is trivial to obtain sparse codes for pixel
values, we propose to consider their non-linear similarities to
perform kernel sparse coding in a high dimensional feature space.
We develop the kernel K-lines clustering procedure for inferring
kernel dictionaries and use the kernel sparse codes to determine
if a pixel belongs to a tumorous region. By incorporating spatial
locality information of the pixels, contiguous tumor regions can
be efficiently identified. A low complexity segmentation approach,
which allows the user to initialize the tumor region, is also
presented. Results show that both of the proposed approaches
lead to accurate tumor identification with a low false positive
rate, when compared to manual segmentation by an expert.
Index Terms—MRI, tumor segmentation, sparse representations,
kernel methods
I. INTRODUCTION
A robust method to automatically segment a medical image
into its constituent heterogeneous regions can be an extremely
valuable tool for clinical diagnosis and disease modeling.
Given a reasonably large data set, performing manual segmentation
is not a practical approach. Brain tumor detection
and segmentation have been of interest to researchers over
the recent years and currently, there exists no comprehensive
algorithm built and adopted in the clinical setting [1]. Although
patient scans can be obtained using different imaging modalities,
Magnetic Resonance Imaging (MRI) has been commonly
adopted for brain imaging over other modalities because of
its non-invasive and non-ionizing nature, and ability for direct
multi-plane imaging.
Tumors may be malignant or benign as determined by a
biopsy, and are known to affect brain symmetry and cause
damages to the surrounding brain tissues. Automated tumor
segmentation approaches are often challenged by the variability
in size, shape and location of the tumor, the high
degree of similarity in the pixel intensities between normal
and abnormal brain tissue regions, and the intensity variations
of identical tissues across volumes. As a result, unsupervised
thresholding techniques have not been very successful in
accurate tumor segmentation [2]. Furthermore, approaches that
incorporate prior knowledge of the normal brain from atlases
require accurate non-rigid registration [3], [4], and hence
generating adequate segmentation results potentially calls for
user-intervention and/or a patient specific training system. In
addition, these methods require elaborate pre-processing and
they tend to over-estimate the tumor volume.
Approaches for tumor segmentation can be either regionbased
or pixel based. The active contours method [5] is a
widely adopted region-based approach that is usually combined
with a level-set evolution for convergence to a region
of interest [6]. However, it is sensitive to the contour initialization,
and has a high computational cost due to its iterative
nature. Model-based approaches [7] employ geometric priors
to extend the Expectation Maximization (EM) algorithm to
augment statistical classification. In relatively homogeneous
cases such as low grade gliomas, the outlier detection framework
proposed by Prastawa et al. [2], [8] was shown to
perform well.
Pixel based approaches such as Fuzzy C-Means (FCM)
using neighborhood labels [9], Conditional Random Fields
[10], Bayesian model-aware affinities extending the SWA
algorithm [1], and the more recent graph-based techniques
combined with Cellular-Automata (CA) algorithm [11] have
also achieved some success in tumor segmentation. However,
processing issues with respect to contour initialization, noise
reduction, intensity standardization, cluster selection, spatial
registration, and the need for accurate manual seed-selection
leaves substantial room for improvement. In addition, building
a robust automated approach that does not require user intervention
is very important, particularly for processing large
datasets.