| 1. |
- Bajic, Buda, et al.
(author)
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Blind restoration of images degraded with mixed poisson-Gaussian noise with application in transmission electron microscopy
- 2016
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In: 2016 Ieee 13Th International Symposium On Biomedical Imaging (ISBI). - : IEEE. - 9781479923496 - 9781479923502 ; , s. 123-127
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Conference paper (peer-reviewed)abstract
- Noise and blur, present in images after acquisition, negatively affect their further analysis. For image enhancement when the Point Spread Function (PSF) is unknown, blind deblurring is suitable, where both the PSF and the original image are simultaneously reconstructed. In many realistic imaging conditions, noise is modelled as a mixture of Poisson (signal-dependent) and Gaussian (signal independent) noise. In this paper we propose a blind deconvolution method for images degraded by such mixed noise. The method is based on regularized energy minimization. We evaluate its performance on synthetic images, for different blur kernels and different levels of noise, and compare with non-blind restoration. We illustrate the performance of the method on Transmission Electron Microscopy images of cilia, used in clinical practice for diagnosis of a particular type of genetic disorders.
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| 2. |
- Källén, Hanna, et al.
(author)
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Towards Grading Gleason Score using Generically Trained Deep convolutional Neural Networks
- 2016
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In: 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI). - : Institute of Electrical and Electronics Engineers (IEEE). - 9781479923496 - 9781479923502 ; 2016-June, s. 1163-1167
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Conference paper (peer-reviewed)abstract
- We developed an automatic algorithm with the purpose to assist pathologists to report Gleason score on malignant prostatic adenocarcinoma specimen. In order to detect and classify the cancerous tissue, a deep convolutional neural network that had been pre-trained on a large set of photographic images was used. A specific aim was to support intuitive interaction with the result, to let pathologists adjust and correct the output. Therefore, we have designed an algorithm that makes a spatial classification of the whole slide into the same growth patterns as pathologists do. The 22-layer network was cut at an earlier layer and the output from that layer was used to train both a random forest classifier and a support vector machines classifier. At a specific layer a small patch of the image was used to calculate a feature vector and an image is represented by a number of those vectors. We have classified both the individual patches and the entire images. The classification results were compared for different scales of the images and feature vectors from two different layers from the network. Testing was made on a dataset consisting of 213 images, all containing a single class, benign tissue or Gleason score 3-5. Using 10-fold cross validation the accuracy per patch was 81 %. For whole images, the accuracy was increased to 89 %.
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| 3. |
- Moreno, Rodrigo, 1973-, et al.
(author)
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Vesselness Estimation through Higher-Order Orientation Tensors
- 2016
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In: International Symposium on Biomedical Imaging (ISBI). - : IEEE Computer Society. - 9781479923502 ; , s. 1139-1142
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Conference paper (peer-reviewed)abstract
- We recently proposed a method for estimating vesselness based on detection of ring patterns in the local distribution ofthe gradient. This method has a better performance than other state-of-the-art algorithms. However, the original implementation of the method makes use of the spherical harmonics transform locally, which is time consuming. In this paper we propose an equivalent formulation of the method based on higher-order tensors. A linear mapping between the spherical harmonics transform and higher-order orientation tensors is used in order to reduce the complexity of the method. With the new implementation, the analysis of computed tomography angiography data can be performed 2.6 times faster compared with the original implementation.
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| 4. |
- Suveer, Amit, et al.
(author)
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Automated detection of cilia in low magnification transmission electron microscopy images using template matching
- 2016
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In: Biomedical Imaging (ISBI), 2016 IEEE 13th International Symposium on. - : IEEE. - 9781479923496 - 9781479923502 ; , s. 386-390
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Conference paper (other academic/artistic)abstract
- Ultrastructural analysis using Transmission Electron Microscopy (TEM) is a common approach for diagnosing primary ciliary dyskinesia. The manually performed diagnostic procedure is time consuming and subjective, and automation of the process is highly desirable. We aim at automating the search for plausible cilia instances in images at low magnification, followed by acquisition of high magnification images of regions with detected cilia for further analysis. This paper presents a template matching based method for automated detection of cilia objects in low magnification TEM images, where object radii do not exceed 10 pixels. We evaluate the performance of a series of synthetic templates generated for this purpose by comparing automated detection with results manually created by an expert pathologist. The best template achieves a detection at equal error rate of 47% which suffices to identify densely populated cilia regions suitable for high magnification imaging.
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