9,881 research outputs found
Automatic Image Segmentation by Dynamic Region Merging
This paper addresses the automatic image segmentation problem in a region
merging style. With an initially over-segmented image, in which the many
regions (or super-pixels) with homogeneous color are detected, image
segmentation is performed by iteratively merging the regions according to a
statistical test. There are two essential issues in a region merging algorithm:
order of merging and the stopping criterion. In the proposed algorithm, these
two issues are solved by a novel predicate, which is defined by the sequential
probability ratio test (SPRT) and the maximum likelihood criterion. Starting
from an over-segmented image, neighboring regions are progressively merged if
there is an evidence for merging according to this predicate. We show that the
merging order follows the principle of dynamic programming. This formulates
image segmentation as an inference problem, where the final segmentation is
established based on the observed image. We also prove that the produced
segmentation satisfies certain global properties. In addition, a faster
algorithm is developed to accelerate the region merging process, which
maintains a nearest neighbor graph in each iteration. Experiments on real
natural images are conducted to demonstrate the performance of the proposed
dynamic region merging algorithm.Comment: 28 pages. This paper is under review in IEEE TI
Brain Tumor Vascular Network Segmentation from Micro-Tomography
Micro-tomography produces high resolution images of bio- logical structures such as vascular networks. In this paper, we present a new approach for segmenting vascular network into pathological and normal regions from considering their micro-vessel 3D structure only. We define and use a condi- tional random field for segmenting the output of a watershed algorithm. The tumoral and normal classes are thus character- ized by their respective distribution of watershed region size interpreted as local vascular territories
Segmentation of ultrasound images of thyroid nodule for assisting fine needle aspiration cytology
The incidence of thyroid nodule is very high and generally increases with the
age. Thyroid nodule may presage the emergence of thyroid cancer. The thyroid
nodule can be completely cured if detected early. Fine needle aspiration
cytology is a recognized early diagnosis method of thyroid nodule. There are
still some limitations in the fine needle aspiration cytology, and the
ultrasound diagnosis of thyroid nodule has become the first choice for
auxiliary examination of thyroid nodular disease. If we could combine medical
imaging technology and fine needle aspiration cytology, the diagnostic rate of
thyroid nodule would be improved significantly. The properties of ultrasound
will degrade the image quality, which makes it difficult to recognize the edges
for physicians. Image segmentation technique based on graph theory has become a
research hotspot at present. Normalized cut (Ncut) is a representative one,
which is suitable for segmentation of feature parts of medical image. However,
how to solve the normalized cut has become a problem, which needs large memory
capacity and heavy calculation of weight matrix. It always generates over
segmentation or less segmentation which leads to inaccurate in the
segmentation. The speckle noise in B ultrasound image of thyroid tumor makes
the quality of the image deteriorate. In the light of this characteristic, we
combine the anisotropic diffusion model with the normalized cut in this paper.
After the enhancement of anisotropic diffusion model, it removes the noise in
the B ultrasound image while preserves the important edges and local details.
This reduces the amount of computation in constructing the weight matrix of the
improved normalized cut and improves the accuracy of the final segmentation
results. The feasibility of the method is proved by the experimental results.Comment: 15pages,13figure
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