| 1. |
- Chandran, P. S., et al.
(author)
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Cluster detection in cytology images using the cellgraph method
- 2012
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In: Information Technology in Medicine and Education (ITME), 2012 International Symposium. - 9781467321099 ; , s. 923-927
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Conference paper (peer-reviewed)abstract
- Automated cervical cancer detection system is primarily based on delineating the cell nuclei and analyzing their textural and morphometric features for malignant characteristics. The presence of cell clusters in the slides have diagnostic value, since malignant cells have a greater tendency to stick together forming clusters than normal cells. However, cell clusters pose difficulty in delineating nucleus and extracting features reliably for malignancy detection in comparison to free lying cells. LBC slide preparation techniques remove biological artifacts and clustering to some extent but not completely. Hence cluster detection in automated cervical cancer screening becomes significant. In this work, a graph theoretical technique is adopted which can identify and compute quantitative metrics for this purpose. This method constructs a cell graph of the image in accordance with the Waxman model, using the positional coordinates of cells. The computed graph metrics from the cell graphs are used as the feature set for the classifier to deal with cell clusters. It is a preliminary exploration of using the topological analysis of the cellgraph to cytological images and the accuracy of classification using SVM showed that the results are well suited for cluster detection.
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| 2. |
- Moshavegh, Ramin, et al.
(author)
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Automated segmentation of free-lying cell nuclei in Pap smears for malignancy-associated change analysis
- 2012
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In: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS. - 1557-170X. - 9781424441198 ; , s. 5372-5375
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Conference paper (peer-reviewed)abstract
- This paper presents an automated algorithm for robustly detecting and segmenting free-lying cell nuclei in bright-field microscope images of Pap smears. This is an essential initial step in the development of an automated screening system for cervical cancer based on malignancy associated change (MAC) analysis. The proposed segmentation algorithm makes use of gray-scale annular closings to identify free-lying nuclei-like objects together with marker-based watershed segmentation to accurately delineate the nuclear boundaries. The algorithm also employs artifact rejection based on size, shape, and granularity to ensure only the nuclei of intermediate squamous epithelial cells are retained. An evaluation of the performance of the algorithm relative to expert manual segmentation of 33 fields-of-view from 11 Pap smear slides is also presented. The results show that the sensitivity and specificity of nucleus detection is 94.71% and 85.30% respectively, and that the accuracy of segmentation, measured using the Dice coefficient, of the detected nuclei is 97.30±1.3%.
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| 3. |
- Nordström, Anna, et al.
(author)
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Higher muscle mass but lower gynoid fat mass in athletes using anabolic androgenic steroids
- 2012
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In: Journal of Strength and Conditioning Research. - 1064-8011 .- 1533-4287. ; 26:1, s. 246-250
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Journal article (peer-reviewed)abstract
- Nordstrom, A, Hogstrom, G, Eriksson, A, Bonnerud, P, Tegner, Y, and Malm, C. Higher muscle mass but lower gynoid fat mass in athletes using anabolic androgenic steroids. J Strength Cond Res 26(1): 246-250, 2012-This study evaluated the relationship between anabolic androgenic steroid (AAS) use and body constitution. Dual-energy x-ray absorptiometry was used to measure bone mineral density (BMD, g.cm(-2)) of the total body, arms, and legs. Total gynoid and android fat mass (grams) and total lean mass (grams) were measured in 10 strength trained athletes (41.4 +/- 7.9 years) who had used AASs for 5-15 years (Doped) and 7 strength trained athletes (29.4 +/- 6.2 years) who had never used AASs (Clean). Seventeen sedentary men (30.3 +/- 2.1 years) served as Controls. Doped athletes had significantly more lean body mass (85.5 +/- 3.8 vs. 75.3 +/- 2.5 vs. 60.7 +/- 1.9, p < 0.001) and a greater index of fat-free/fat mass (5.8 vs. 2.6 vs. 2.5, p < 0.001) compared with Clean athletes and Controls. Doped athletes also had significantly less gynoid fat mass compared with that of Clean athletes (2.8 +/- 0.4 vs. 4.8 +/- 0.2 kg, p = 0.02). There were no differences in BMD between the athletes (p = 0.39-0.98), but both groups had significantly higher BMDs at all sites compared with that of Controls (p = 0.01 to <0.001). Thus, long-term AAS use seems to alter body constitution, favoring higher muscle mass and reduced gynoid fat mass without affecting BMD.
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