39,471 research outputs found
Combined segmentation and classificationbased approach to automated analysis of biomedical signals obtained from calcium imaging
Automated screening systems in conjunction with machine learning-based methods are becoming an essential part of the healthcare systems for assisting in disease diagnosis. Moreover, manually annotating data and hand-crafting features for training purposes are impractical and time-consuming. We propose a segmentation and classification-based approach for assembling an automated screening system for the analysis of calcium imaging. The method was developed and verified using the effects of disease IgGs (from Amyotrophic Lateral Sclerosis patients) on calcium (Ca2+) homeostasis. From 33 imaging videos we analyzed, 21 belonged to the disease and 12 to the control experimental groups. The method consists of three main steps: projection, segmentation, and classification. The entire Ca2+ time-lapse image recordings (videos) were projected into a single image using different projection methods. Segmentation was performed by using a multi-level thresholding (MLT) step and the Regions of Interest (ROIs) that encompassed cell somas were detected. A mean value of the pixels within these boundaries was collected at each time point to obtain the Ca2+ traces (time-series). Finally, a new matrix called feature image was generated from those traces and used for assessing the classification accuracy of various classifiers (control vs. disease). The mean value of the segmentation F-score for all the data was above 0.80 throughout the tested threshold levels for all projection methods, namely maximum intensity, standard deviation, and standard deviation with linear scaling projection. Although the classification accuracy reached up to 90.14%, interestingly, we observed that achieving better scores in segmentation results did not necessarily correspond to an increase in classification performance. Our method takes the advantage of the multi-level thresholding and of a classification procedure based on the feature images, thus it does not have to rely on hand- crafted training parameters of each event. It thus provides a semi-autonomous tool for assessing segmentation parameters which allows for the best classification accuracy
Automated Detection of Regions of Interest for Brain Perfusion MR Images
Images with abnormal brain anatomy produce problems for automatic
segmentation techniques, and as a result poor ROI detection affects both
quantitative measurements and visual assessment of perfusion data. This paper
presents a new approach for fully automated and relatively accurate ROI
detection from dynamic susceptibility contrast perfusion magnetic resonance and
can therefore be applied excellently in the perfusion analysis. In the proposed
approach the segmentation output is a binary mask of perfusion ROI that has
zero values for air pixels, pixels that represent non-brain tissues, and
cerebrospinal fluid pixels. The process of binary mask producing starts with
extracting low intensity pixels by thresholding. Optimal low-threshold value is
solved by obtaining intensity pixels information from the approximate
anatomical brain location. Holes filling algorithm and binary region growing
algorithm are used to remove falsely detected regions and produce region of
only brain tissues. Further, CSF pixels extraction is provided by thresholding
of high intensity pixels from region of only brain tissues. Each time-point
image of the perfusion sequence is used for adjustment of CSF pixels location.
The segmentation results were compared with the manual segmentation performed
by experienced radiologists, considered as the reference standard for
evaluation of proposed approach. On average of 120 images the segmentation
results have a good agreement with the reference standard. All detected
perfusion ROIs were deemed by two experienced radiologists as satisfactory
enough for clinical use. The results show that proposed approach is suitable to
be used for perfusion ROI detection from DSC head scans. Segmentation tool
based on the proposed approach can be implemented as a part of any automatic
brain image processing system for clinical use
Self-Configuring and Evolving Fuzzy Image Thresholding
Every segmentation algorithm has parameters that need to be adjusted in order
to achieve good results. Evolving fuzzy systems for adjustment of segmentation
parameters have been proposed recently (Evolving fuzzy image segmentation --
EFIS [1]. However, similar to any other algorithm, EFIS too suffers from a few
limitations when used in practice. As a major drawback, EFIS depends on
detection of the object of interest for feature calculation, a task that is
highly application-dependent. In this paper, a new version of EFIS is proposed
to overcome these limitations. The new EFIS, called self-configuring EFIS
(SC-EFIS), uses available training data to auto-configure the parameters that
are fixed in EFIS. As well, the proposed SC-EFIS relies on a feature selection
process that does not require the detection of a region of interest (ROI).Comment: To appear in proceedings of The 14th International Conference on
Machine Learning and Applications (IEEE ICMLA 2015), Miami, Florida, USA,
201
The curvelet transform for image denoising
We describe approximate digital implementations of two new mathematical transforms, namely, the ridgelet transform and the curvelet transform. Our implementations offer exact reconstruction, stability against perturbations, ease of implementation, and low computational complexity. A central tool is Fourier-domain computation of an approximate digital Radon transform. We introduce a very simple interpolation in the Fourier space which takes Cartesian samples and yields samples on a rectopolar grid, which is a pseudo-polar sampling set based on a concentric squares geometry. Despite the crudeness of our interpolation, the visual performance is surprisingly good. Our ridgelet transform applies to the Radon transform a special overcomplete wavelet pyramid whose wavelets have compact support in the frequency domain. Our curvelet transform uses our ridgelet transform as a component step, and implements curvelet subbands using a filter bank of a` trous wavelet filters. Our philosophy throughout is that transforms should be overcomplete, rather than critically sampled. We apply these digital transforms to the denoising of some standard images embedded in white noise. In the tests reported here, simple thresholding of the curvelet coefficients is very competitive with "state of the art" techniques based on wavelets, including thresholding of decimated or undecimated wavelet transforms and also including tree-based Bayesian posterior mean methods. Moreover, the curvelet reconstructions exhibit higher perceptual quality than wavelet-based reconstructions, offering visually sharper images and, in particular, higher quality recovery of edges and of faint linear and curvilinear features. Existing theory for curvelet and ridgelet transforms suggests that these new approaches can outperform wavelet methods in certain image reconstruction problems. The empirical results reported here are in encouraging agreement
Improvement of BM3D Algorithm and Employment to Satellite and CFA Images Denoising
This paper proposes a new procedure in order to improve the performance of
block matching and 3-D filtering (BM3D) image denoising algorithm. It is
demonstrated that it is possible to achieve a better performance than that of
BM3D algorithm in a variety of noise levels. This method changes BM3D algorithm
parameter values according to noise level, removes prefiltering, which is used
in high noise level; therefore Peak Signal-to-Noise Ratio (PSNR) and visual
quality get improved, and BM3D complexities and processing time are reduced.
This improved BM3D algorithm is extended and used to denoise satellite and
color filter array (CFA) images. Output results show that the performance has
upgraded in comparison with current methods of denoising satellite and CFA
images. In this regard this algorithm is compared with Adaptive PCA algorithm,
that has led to superior performance for denoising CFA images, on the subject
of PSNR and visual quality. Also the processing time has decreased
significantly.Comment: 11 pages, 7 figur
Approximate Lesion Localization in Dermoscopy Images
Background: Dermoscopy is one of the major imaging modalities used in the
diagnosis of melanoma and other pigmented skin lesions. Due to the difficulty
and subjectivity of human interpretation, automated analysis of dermoscopy
images has become an important research area. Border detection is often the
first step in this analysis. Methods: In this article, we present an
approximate lesion localization method that serves as a preprocessing step for
detecting borders in dermoscopy images. In this method, first the black frame
around the image is removed using an iterative algorithm. The approximate
location of the lesion is then determined using an ensemble of thresholding
algorithms. Results: The method is tested on a set of 428 dermoscopy images.
The localization error is quantified by a metric that uses dermatologist
determined borders as the ground truth. Conclusion: The results demonstrate
that the method presented here achieves both fast and accurate localization of
lesions in dermoscopy images
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