This paper describes a tracking method to trace the movements of fluorescently labeled mitochondria in time-lapse image sequences. It is based on particle filtering, which is a state-of-the-art tracking method, and is enhanced with a spatial constraint to improve robustness. Since mitochondria move only through axons, the spatial constraint is generated by axon segmentation on a single frame, which is the average of all the frames. The spatial constraint limits the search space of the state vector and, consequently, lowers the chance for the tracking to get lost. Using a background subtraction algorithm, the proposed method is also equipped with automatic detection of starting points, thus minimizing the requirement for user input. Implementation of the proposed method for tracking of fluorescently labeled mitochondria in time-lapse images showed substantially improved robustness and speed compared to a conventional method. With these improvements, this new particle tracking method is expected to increase the throughput of fluorescently labeled mitochondrial transport experiments, which are required for neuroscience research.
We propose a cartilage matching technique based on the registration of the corresponding bone structures instead of
using the cartilage. Our method consists of five steps. First, cartilage and corresponding bone structures are extracted by
semi-automatic segmentation. Second, gross translational mismatch between corresponding bone structures is corrected
by point-based rough registration. The center of inertia (COI) of each segmented bone structure is considered as the
reference point. Third, the initial alignment is refined by distance-based surface registration. For fast and robust
convergence of the distance measure to the optimal value, a 3D distance map is generated by the Gaussian-weighted
narrow-band distance propagation. Fourth, rigid transformation of the bone surface registration is applied to the cartilage
of baseline MR images. Finally, morphological differences of the corresponding cartilages are visualized by color-coded
mapping and image fusion. Experimental results show that the cartilage morphological changes of baseline and follow-up
MR knee images can be easily recognized by the correct registration of the corresponding bones.
Osteoarthritis (OA) is associated with degradation of cartilage and related changes in the underlying bone. Quantitative
measurement of those changes from MR images is an important biomarker to study the progression of OA and it requires
a reliable segmentation of knee bone and cartilage. As the most popular method, manual segmentation of knee joint
structures by boundary delineation is highly laborious and subject to user-variation. To overcome these difficulties, we
have developed a semi-automated method for segmentation of knee bones, which consisted of two steps: placement of
seeds and computation of segmentation. In the first step, seeds were placed by the user on a number of slices and then
were propagated automatically to neighboring images. The seed placement could be performed on any of sagittal,
coronal, and axial planes. The second step, computation of segmentation, was based on a graph-cuts algorithm where the
optimal segmentation is the one that minimizes a cost function, which integrated the seeds specified by the user and both
the regional and boundary properties of the regions to be segmented. The algorithm also allows simultaneous
segmentation of three compartments of the knee bone (femur, tibia, patella). Our method was tested on the knee MR
images of six subjects from the osteoarthritis initiative (OAI). The segmentation processing time (mean±SD) was
(22±4)min, which is much shorter than that by the manual boundary delineation method (typically several hours). With
this improved efficiency, our segmentation method will facilitate the quantitative morphologic analysis of changes in
knee bones associated with osteoarthritis.
Knee osteoarthritis is the most common debilitating health condition affecting elderly population. MR imaging of the
knee is highly sensitive for diagnosis and evaluation of the extent of knee osteoarthritis. Quantitative analysis of the
progression of osteoarthritis is commonly based on segmentation and measurement of articular cartilage from knee MR
images. Segmentation of the knee articular cartilage, however, is extremely laborious and technically demanding,
because the cartilage is of complex geometry and thin and small in size. To improve precision and efficiency of the
segmentation of the cartilage, we have applied a semi-automated segmentation method that is based on an s/t graph cut
algorithm. The cost function was defined integrating regional and boundary cues. While regional cues can encode any
intensity distributions of two regions, "object" (cartilage) and "background" (the rest), boundary cues are based on the
intensity differences between neighboring pixels. For three-dimensional (3-D) segmentation, hard constraints are also
specified in 3-D way facilitating user interaction. When our proposed semi-automated method was tested on clinical
patients' MR images (160 slices, 0.7 mm slice thickness), a considerable amount of segmentation time was saved with
improved efficiency, compared to a manual segmentation approach.
Recently ground glass opacities (GGOs) have become noteworthy in lung cancer diagnosis. It is crucial to define the boundary a GGO accurately and consistently, since the growth rate is the most manifest evidence of its malignancy. The indefinite and irregular boundary of a GGO makes deformable models adequate for its segmentation. Among deformable models a level set method has the ability to handle topological changes. For the exact estimation of GGO's volume change, the pulmonary airways inside GGO should be excluded in its volume estimation, which necessitate the segmentation into more regions than two of the object and the background. Hence, we adopted a multi-phase deformable model of two level set functions and modified its energy functional into an asymmetric form. The main two modifications are the elimination of one region in four regions of the conventional 4-phase deformable model and the prevention of the outer region from spreading out of the initialization. The proposed model segments the input image into three regions of the inner and outer regions, and the background. The GGO tissues are segmented as the inner region and the outer region plays the role of blockade for the inner region not to leak out to adjacent anatomical structures of similar Hounsfield Unit (HU) values. Our experimental results confirmed the feasibility of the proposed method as a pre-processing step for three dimensional (3-D) volume measurement of the GGO.
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