Ultrasound (US) is a versatile, low cost, real-time, widely available imaging modality. Manual segmentation for volumetric US measurements can be difficult and very time consuming, requiring slice-by-slice segmentations. However, automatic segmentation of ultrasound images can prove challenging due to the presence of speckle, attenuation, missing boundaries, signal dropouts, and artefacts. Semi-automatic segmentation techniques can improve the speed and accuracy of such measurements, taking advantage of clinical expertise while allowing user interaction. This paper presents a novel solution for interactive image segmentation on B-mode ultrasound images. The proposed method builds on the Live Wire framework and introduces two new sets of Live Wire costs, namely a Feature Asymmetry (FA) cost to localise edges and a weak shape constraint cost to aid the selection of appropriate boundaries in the presence of missing information or artefacts. The resulting semi-automatic segmentation method follows edges based on structural relevance rather than intensity gradients, adapting the method to ultrasound images, where the object boundaries are normally fuzzy. The new method is applied in the context of fetal arm adipose tissue quantification, the adipose tissue being an indicator of the fetal nutritional state. A quantitative and qualitative evaluation is performed with respect to related segmentation techniques. The method was tested on 48 manually segmented ultrasound images of the fetal arm across gestation, showing similar accuracy to the intensity-based Live Wire approach but superior repeatability while requiring significantly less time and user interaction.
In recent years, fetal diagnostics have relied heavily on clinical assessment and biometric analysis of manually acquired
ultrasound images. There is a profound need for automated and standardized evaluation tools to characterize fetal growth
and development. This work addresses this need through the novel use of feature-based techniques to develop evaluators
of fetal brain gestation. The methodology is comprised of an automated database-driven 2D/3D image atlas construction
method, which includes several iterative processes. A unique database was designed to store fetal image data acquired as
part of the Intergrowth-21st study. This database drives the proposed automated atlas construction methodology using
local phase information to perform affine registration with normalized mutual information as the similarity parameter,
followed by wavelet-based image fusion and averaging. The unique feature-based application of local phase and wavelet
fusion towards creating the atlas reduces the intensity dependence and difficulties in registering ultrasound images. The
method is evaluated on fetal transthalamic head ultrasound images of 20 weeks gestation. The results show that the
proposed method is more robust to intensity variations than standard intensity-based methods. Results also suggest that
the feature-based approach improves the registration accuracy needed in creating a clinically valid ultrasound image
atlas.
Landmark based statistical object modeling techniques, such as Active Shape Model (ASM), have proven useful in
medical image analysis. Identification of the same homologous set of points in a training set of object shapes is the
most crucial step in ASM, which has encountered challenges such as (C1) defining and characterizing landmarks;
(C2) ensuring homology; (C3) generalizing to n > 2 dimensions; (C4) achieving practical computations. In this
paper, we propose a novel global-to-local strategy that attempts to address C3 and C4 directly and works in Rn.
The 2D version starts from two initial corresponding points determined in all training shapes via a method α, and
subsequently by subdividing the shapes into connected boundary segments by a line determined by these points.
A shape analysis method β is applied on each segment to determine a landmark on the segment. This point
introduces more pairs of points, the lines defined by which are used to further subdivide the boundary segments.
This recursive boundary subdivision (RBS) process continues simultaneously on all training shapes, maintaining
synchrony of the level of recursion, and thereby keeping correspondence among generated points automatically
by the correspondence of the homologous shape segments in all training shapes. The process terminates when
no subdividing lines are left to be considered that indicate (as per method β) that a point can be selected on
the associated segment. Examples of α and β are presented based on (a) distance; (b) Principal Component
Analysis (PCA); and (c) the novel concept of virtual landmarks.
Scale is a fundamental concept in computer vision and pattern recognition, especially in the fields of shape analysis, image segmentation, and registration. It represents the level of detail of object information in scenes. Global scale methods in image processing process the scene at each of various fixed scales and combine the results, as in scale space approaches. Local scale approaches define the largest homogeneous region at each point, and treat these as fundamental units. A similar dichotomy exists for describing shapes also. To vary the level of detail depending on application, it is desirable to be able to detect dominant points on shape boundaries at different scales. In this paper, we compare global and local scale approaches to shape analysis. For global scale, the Curvature Scale Space (CSS) method is selected, which is a state of the art shape descriptor, and is used in the MPEG-7 standard. The local scale approach is based on the notion of curvature-scale (c-scale), which is a new local scale concept that brings the idea of local morphometric scale (such as ball-, tensor-, and generalized scale) developed for images to the realm of boundaries. All previous methods of extracting dominant points lack this concept of a local scale. In this paper, we present a thorough evaluation of these global and local scale methods. Our analysis indicates that locally adaptive scale has advantages over global scale in shape description, just as it has also been demonstrated in image filtering, segmentation, and registration.
Model-based segmentation approaches, such as those employing Active Shape Models (ASMs), have proved to be
useful for medical image segmentation and understanding. To build the model, however, we need an annotated
training set of shapes wherein corresponding landmarks are identified in every shape. Manual positioning of
landmarks is a tedious, time consuming, and error prone task, and almost impossible in the 3D space. In an
attempt to overcome some of these drawbacks, we have devised several automatic methods under two approaches:
c-scale based and shape variance based. The c-scale based methods use the concept of local curvature to find
landmarks on the mean shape of the training set. These landmarks are then propagated to all the shapes of
the training set to establish correspondence in a local-to-global manner. The variance-based method is guided
by the strategy of equalization of the shape variance contained in the training set for selecting landmarks. The
main premise here is that this strategy itself takes care of the correspondence issue and at the same time deploys
landmarks very frugally and optimally considering shape variations. The desired landmarks are positioned
around each contour so as to equally distribute the total variance existing in the training set in a global-to-local
manner. The methods are evaluated on 40 MRI foot data sets and compared in terms of compactness. The
results show that, for the same number of landmarks, the proposed methods are more compact than manual and
equally spaced methods of annotation, and the variance equalization method tops the list.
Landmark based statistical object modeling techniques, such as Active Shape Modeling, have proven useful in
medical image analysis. Identification of the same homologous set of points in a training set of object shapes is the
most crucial step in ASM, which has encountered challenges, the most crucial among these being (C1) defining
and characterizing landmarks; (C2) ensuring homology; (C3) generalizing to n > 2 dimensions; (C4) achieving
practical computations. In this paper, we propose a novel global-to-local strategy that attempts to address C3
and C4 directly and works in Rn. The 3D version of it attempts to address C1 and C2 indirectly by starting
from three initial corresponding points determined in all training shapes via a method α, and subsequently by
subdividing the shapes into connected boundary segments by a plane determined by these points. A shape
analysis method β is applied on each segment to determine a landmark on the segment. This point introduces
more triplets of points, the planes defined by which are used to further subdivide the boundary segments.
This recursive boundary subdivision (RBS) process continues simultaneously on all training shapes, maintaining
synchrony of the level of recursion, and thereby keeping correspondence among generated points automatically
by the correspondence of the homologous shape segments in all training shapes. The process terminates when
no subdividing planes are left to be considered that indicate (as per method β) that a point can continue to be
selected on the associated segment. Several examples of α and β are provided as well as some preliminary results
on 3D shapes.
KEYWORDS: Atrial fibrillation, Image segmentation, 3D modeling, Bone, Medical imaging, Magnetic resonance imaging, Bismuth, Digital filtering, Shape analysis, Image processing
Segmentation of organs in medical images is a difficult task requiring very often the use of model-based approaches.
To build the model, we need an annotated training set of shape examples with correspondences
indicated among shapes. Manual positioning of landmarks is a tedious, time-consuming, and error prone task,
and almost impossible in the 3D space. To overcome some of these drawbacks, we devised an automatic method
based on the notion of c-scale, a new local scale concept. For each boundary element b, the arc length of the
largest homogeneous curvature region connected to b is estimated as well as the orientation of the tangent at b.
With this shape description method, we can automatically locate mathematical landmarks selected at different
levels of detail. The method avoids the use of landmarks for the generation of the mean shape. The selection of
landmarks on the mean shape is done automatically using the c-scale method. Then, these landmarks are propagated
to each shape in the training set, defining this way the correspondences among the shapes. Altogether
12 strategies are described along these lines. The methods are evaluated on 40 MRI foot data sets, the object of
interest being the talus bone. The results show that, for the same number of landmarks, the proposed methods
are more compact than manual and equally spaced annotations. The approach is applicable to spaces of any
dimensionality, although we have focused in this paper on 2D shapes.
A new boundary shape description based on the notion of curvature-scale is presented. This shape descriptor performs better than the commonly used Rosenfeld's method of curvature estimation and can be applied directly to digital boundaries without requiring prior approximations. It can extract special points of interest such as convex and concave corners, straight lines, circular segments, and inflection points. The results show that this method produces a complete boundary shape description capable of handling different levels of shape detail. It also has numerous potential applications such as automatic landmark tagging which becomes necessary to build model-based approaches toward the goal of organ modelling and segmentation.
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