We propose a novel method for nonrigid registration of whole coronary artery sequence models with periodic displacement field. 2D nonrigid registration method is proposed that periodic deformed information is applied into correspondence with whole fluoroscopic angiograms. The proposed methodology is divided into two parts: one cycle's nonrigid registration, spreading periodic displacement information into other cycles. In the first part, a nonrigid registration method of one cycle is implemented and used to compensate for any local shape discrepancy. In the second part, periodic displacement field is spreading into images on other cycles in order to align the whole sequence. Experimental evaluation conducted on a set of 9 fluoroscopic angiograms results in a reduced target registration error, which showed the effectiveness of the proposed methodology.
We propose a novel method for nonrigid registration of coronary arteries within frames of a fluoroscopic X-ray angiogram sequence with propagated deformation field. The aim is to remove the motion of coronary arteries in order to simplify further registration of the 3D vessel structure obtained from computed tomography angiography, with the x-ray sequence. The Proposed methodology comprises two stages: propagated adjacent pairwise nonrigid registration, and, sequence-wise fixed frame nonrigid registration. In the first stage, a propagated nonrigid transformation reduces the disparity search range for each frame sequentially. In the second stage, nonrigid registration is applied for all frames with a fixed target frame, thus generating a motion-aligned sequence. Experimental evaluation conducted on a set of 7 fluoroscopic angiograms resulted in reduced target registration error, compared to previous methods, showing the effectiveness of the proposed methodology.
We present a new method for automatic detection of micro-calcifications using the Discriminative Restricted Boltzmann Machine (DRBM). The DRBM is used to automatically learn the specific features which distinguish micro-calcifications from normal tissue as well as their morphological variations. Within the DRBM, low level image structures that are specific features of micro-calcifications are automatically captured without any appropriate feature selection based on expert knowledge or time-consuming hand-tuning, which was required for previous methods. Experimental evaluation conducted on a set of 33 mammograms gave a result of area under Receiver Operating Characteristics (ROC) curve 0.8294, which showed the effectiveness of the proposed method.
Segmentation of bone and cartilage from a three dimensional knee magnetic resonance (MR) image is a crucial
element in monitoring and understanding of development and progress of osteoarthritis. Until now, various
segmentation methods have been proposed to separate the bone from other tissues, but it still remains challenging
problem due to different modality of MR images, low contrast between bone and tissues, and shape irregularity.
In this paper, we present a new fully-automatic segmentation method of bone compartments using relevant bone
atlases from a training set. To find the relevant bone atlases and obtain the segmentation, a coarse-to-fine
strategy is proposed. In the coarse step, the best atlas among the training set and an initial segmentation are
simultaneously detected using branch and bound tree search. Since the best atlas in the coarse step is not
accurately aligned, all atlases from the training set are aligned to the initial segmentation, and the best aligned
atlas is selected in the middle step. Finally, in the fine step, segmentation is conducted as adaptively integrating
shape of the best aligned atlas and appearance prior based on characteristics of local regions. For experiment,
femur and tibia bones of forty test MR images are segmented by the proposed method using sixty training MR
images. Experimental results show that a performance of the segmentation and the registration becomes better
as going near the fine step, and the proposed method obtain the comparable performance with the state-of-the-art
methods.
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.
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