10,357 research outputs found
A robust lesion boundary segmentation algorithm using level set methods
This paper addresses the issue of accurate lesion segmentation in retinal imagery, using level set methods and
a novel stopping mechanism - an elementary features scheme. Specifically, the curve propagation is guided
by a gradient map built using a combination of histogram equalization and robust statistics. The stopping
mechanism uses elementary features gathered as the curve deforms over time, and then using a lesionness
measure, defined herein, ’looks back in time’ to find the point at which the curve best fits the real object.
We compare the proposed method against five other
segmentation algorithms performed on 50 randomly selected images of exudates with a database of clinician
demarcated boundaries as ground truth
Lesion boundary segmentation using level set methods
This paper addresses the issue of accurate lesion segmentation in retinal imagery, using level set methods and
a novel stopping mechanism - an elementary features scheme. Specifically, the curve propagation is guided by a gradient map built using a combination of histogram equalization and robust statistics. The stopping mechanism uses elementary features gathered as the curve deforms over time, and then using a lesionness measure, defined herein, ’looks back in time’ to find the point at which the curve best fits the real object. We implement the level set using a fast upwind scheme and compare the proposed method against five other
segmentation algorithms performed on 50 randomly selected images of exudates with a database of clinician
marked-up boundaries as ground truth
Color image restoration with Fuzzy Gaussian mixture model driven nonlocal filter
© Springer International Publishing Switzerland 2015. Color image denoising is one of the classical image processing problem and various techniques have been explored over the years. Recently, nonlocal means (NLM) filter is proven to obtain good results for denoising Gaussian noise corrupted digital images using weighted mean among similar patches. In this paper, we consider fuzzy Gaussian mixture model (GMM) based NLM method for removing mixed Gaussian and impulse noise. By computing an automatic homogeneity map we identify impulse noise locations and utilize an adaptive patch size. Experimental results on mixed noise affected color images show that our scheme performs better than NLM, anisotropic diffusion and GMM-NLM over different noise levels. Comparison with respect to structural similarity, color image difference, and peak signal to noise ratio error metrics are undertaken and our scheme performs well overall without generating color artifacts
Fuzzy Fibers: Uncertainty in dMRI Tractography
Fiber tracking based on diffusion weighted Magnetic Resonance Imaging (dMRI)
allows for noninvasive reconstruction of fiber bundles in the human brain. In
this chapter, we discuss sources of error and uncertainty in this technique,
and review strategies that afford a more reliable interpretation of the
results. This includes methods for computing and rendering probabilistic
tractograms, which estimate precision in the face of measurement noise and
artifacts. However, we also address aspects that have received less attention
so far, such as model selection, partial voluming, and the impact of
parameters, both in preprocessing and in fiber tracking itself. We conclude by
giving impulses for future research
Using shape entropy as a feature to lesion boundary segmentation with level sets
Accurate lesion segmentation in retinal imagery is an area of vast research. Of the many segmentation methods
available very few are insensitive to topological changes on noisy surfaces. This paper presents an extension to
earlier work on a novel stopping mechanism for level sets. The elementary features scheme (ELS) in [5] is
extended to include shape entropy as a feature used to ’look back in time’ and find the point at which the curve
best fits the real object. We compare the proposed extension against the original algorithm for timing and
accuracy using 50 randomly selected images of exudates with a database of clinician demarcated boundaries as
ground truth. While this work is presented applied to medical imagery, it can be used for any application
involving the segmentation of bright or dark blobs on noisy images
Color image segmentation using a spatial k-means clustering algorithm
This paper details the implementation of a new adaptive technique for color-texture segmentation that is a generalization of the standard K-Means algorithm. The standard K-Means algorithm produces accurate segmentation results only when applied to images defined by homogenous regions with respect to texture and color since no local constraints are applied to impose spatial continuity. In addition, the initialization of the K-Means algorithm is problematic and usually the initial cluster centers are randomly picked. In this paper we detail the implementation of a novel technique to select the dominant colors from the input image using the information from the color histograms. The main contribution of this work is the generalization of the K-Means algorithm that includes the primary features that describe the color smoothness and texture complexity in the process of pixel assignment. The resulting color segmentation scheme has been applied to a large number of natural images and the experimental data indicates the robustness of the new developed segmentation algorithm
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