| 1. |
- Brandl, Miriam B, et al.
(författare)
-
Application of Fuzzy c-Means and Joint-Feature-Clustering to Detect Redundancies of Image-Features in Drug Combinations Studies of Breast Cancer
- 2011
-
Ingår i: AIP Conference Proceedings. - : AIP. - 0094-243X.
-
Konferensbidrag (refereegranskat)abstract
- The high dimensionality of image‐based dataset can be a drawback for classification accuracy. In this study, we propose the application of fuzzy c‐means clustering, cluster validity indices and the notation of a joint‐feature‐clustering matrix to find redundancies of image‐features. The introduced matrix indicates how frequently features are grouped in a mutual cluster. The resulting information can be used to find data‐derived feature prototypes with a common biological meaning, reduce data storage as well as computation times and improve the classification accuracy
|
|
| 2. |
- Pham, Tuan D
(författare)
-
Brain lesion detection in MRI with fuzzy and geostatistical models
- 2010
-
Konferensbidrag (refereegranskat)abstract
- Automated image detection of white matter changes of the brain is essentially helpful in providing a quantitative measure for studying the association of white matter lesions with other types of biomedical data. Such study allows the possibility of several medical hypothesis validations which lead to therapeutic treatment and prevention. This paper presents a new clustering-based segmentation approach for detecting white matter changes in magnetic resonance imaging with particular reference to cognitive decline in the elderly. The proposed method is formulated using the principles of fuzzy c-means algorithm and geostatistics.
|
|
| 3. |
- Pham, Tuan D.
(författare)
-
Entropy rates of physiological aging on microscopy
- 2016
-
Ingår i: Proceedings of 2016 IEEE Symposium Series on Computational Intelligence. - : Institute of Electrical and Electronics Engineers (IEEE). - 9781509042401 - 9781509042418
-
Konferensbidrag (refereegranskat)abstract
- This paper presents a method for computing entropy rates of images by modeling a stationary Markov chain constructed from a weighted graph. The proposed method was applied to the quantification of the complex behavior of the growing rates of physiological aging of Caenorhabditis elegans (C. elegans) on microscopic images, which has been considered as one of the most challenging problems in the search for metrics that can be used for identifying differences among stages in high- throughput and high-content images of physiological aging.
|
|
| 4. |
- Pham, Tuan D
(författare)
-
Medical image restoration using multiple-point geostatistics
- 2010
-
Ingår i: 2010 3rd International Conference on Biomedical Engineering and Informatics (BMEI 2010). - : IEEE. - 9781424464951 ; , s. 371-374
-
Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- Noise inherently exists in medical and biological images as any imaging device, by a finite exposure time, is subject to stochastic noise from the random arrival events of photons. The purpose of image restoration is to bring back as much as possible the original image from its degraded state. This paper presents a spatial multiple-point statistical approach for restoration of medical image degradation.
|
|
| 5. |
- Pham, Tuan D
(författare)
-
Supervised restoration of degraded medical images using multiple-point geostatistics
- 2012
-
Ingår i: Computer Methods and Programs in Biomedicine. - : Elsevier. - 0169-2607 .- 1872-7565. ; 106:3, s. 201-209
-
Tidskriftsartikel (refereegranskat)abstract
- Reducing noise in medical images has been an important issue of research and development for medical diagnosis, patient treatment, and validation of biomedical hypotheses. Noise inherently exists in medical and biological images due to the acquisition and transmission in any imaging devices. Being different from image enhancement, the purpose of image restoration is the process of removing noise from a degraded image in order to recover as much as possible its original version. This paper presents a statistically supervised approach for medical image restoration using the concept of multiple-point geostatistics. Experimental results have shown the effectiveness of the proposed technique which has potential as a new methodology for medical and biological image processing.
|
|
| 6. |
- Su, Ran, et al.
(författare)
-
Junction detection for linear structures
- 2011
-
Ingår i: AIP Conference Proceedings. - : AIP. - 0094-243X.
-
Konferensbidrag (refereegranskat)abstract
- This paper presents a method for detecting junctions in an image with linear structures. The candidate junction points are selected through the combination of correlation matrix and Hessian information; then the branches of the junctions are found according to the intensity information and the correlation value between intensity profile of cross sections and a Gaussian‐shaped template. Junction detection results for neurite images are provided.
|
|
| 7. |
- Tan, Xiao, et al.
(författare)
-
Tree structural watershed for stereo matching
- 2012
-
Ingår i: IVCNZ '12, Proceedings of the 27th Conference on Image and Vision Computing New Zealand. - New York, NY, USA : ACM Digital Library. - 9781450314732 ; , s. 340-345
-
Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- We present a new method for dense stereo matching based on a tree structural cost volume watershed (TSCVW) and a region combination (RC) process. Given a cost volume as the data cost and an initial segmentation result, the proposed TSCVW method reliably estimates the disparities in a segment by using energy optimization to control plane segmentation and plane fitting. Then the disparities in the incorrectly fitted and occluded regions are refined using our RC process. Experimental results show that our method is very robust to different initial segmentation results and the shape of a segment. The comparison between our algorithm and the current state-of-the-art algorithms on the Middlebury website shows that our algorithm is very competitive.
|
|
| 8. |
- Xu, Jin Wei, et al.
(författare)
-
A double thresholding method for cancer stem cell detection
- 2011
-
Konferensbidrag (refereegranskat)abstract
- Image analysis of cancer cells is important for cancer diagnosis and therapy, because it recognized as the most efficient and effective way to observe its proliferation. For the purpose of adaptive and accurate cancer cell image segmentation, a double threshold segmentation method is proposed in this paper. Based on a single gray-value histogram of the RGB color space, a double threshold, the key parameters of threshold segmentation can be fixed by a fitted-curve of the RGB component histogram. As reasonable thresholds confirmed, binary segmentation dependent on two thresholds, will be put into practice and result in binary image. With the post-processing of mathematical morphology and division of whole image, the better segmentation result can be finally achieved. By the comparison with other advanced segmentation methods such as level set and active contour, the proposed double thresholding has been found as the simplest strategy with shortest processing time as well as highest accuracy. The proposed method can be effectively used in the detection and recognition of cancer stem cells in images.
|
|
| 9. |
- Yu, Donggang, et al.
(författare)
-
Recognition and analysis of cell nuclear phases for high-content screening based on morphological features
- 2009
-
Ingår i: NOVA. The University of Newcastle’s Digital Repository. - : Elsevier BV. - 0031-3203. ; 42:4, s. 498-508
-
Tidskriftsartikel (refereegranskat)abstract
- Automated analysis of molecular images has increasingly become an important research in computational life science. In this paper some new and efficient algorithms for recognizing and analyzing cell phases of high-content screening are presented. The conceptual frameworks are based on the morphological features of cell nuclei. The useful preprocessing includes: smooth following and linearization; extraction of morphological structural points; shape recognition based morphological structure; issue of touching cells for cell separation and reconstruction. Furthermore, the novel detecting and analyzing strategies of feed-forward and feed-back of cell phases are proposed based on gray feature, cell shape, geometrical features and difference information of corresponding neighbor frames. Experiment results tested the efficiency of the new method.
|
|
| 10. |
- Zhang, Chao, et al.
(författare)
-
Clustered nuclei splitting using curvature information
- 2011
-
Konferensbidrag (refereegranskat)abstract
- Automated splitting of clustered nuclei from images of tissue sections is essential to many biomedical studies. Many existing image segmentation methods tend to produce over-segmented or under-segmented results for clustered nuclei images. In this paper, a new curvature information based image segmentation algorithm is proposed. Through combining curvature information with a distance map, our algorithm can extract correct markers corresponding to each nucleus. Afterwards, marker based watershed segmentation is used to segment the clustered nuclei. The algorithm is tested on both synthetic and real images. Experimental results show that our algorithm is accurate and robust to noise in segmentation of clustered nuclei.
|
|
