6 research outputs found

    A topological sampling theorem for Robust boundary reconstruction and image segmentation

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    AbstractExisting theories on shape digitization impose strong constraints on admissible shapes, and require error-free data. Consequently, these theories are not applicable to most real-world situations. In this paper, we propose a new approach that overcomes many of these limitations. It assumes that segmentation algorithms represent the detected boundary by a set of points whose deviation from the true contours is bounded. Given these error bounds, we reconstruct boundary connectivity by means of Delaunay triangulation and ฮฑ-shapes. We prove that this procedure is guaranteed to result in topologically correct image segmentations under certain realistic conditions. Experiments on real and synthetic images demonstrate the good performance of the new method and confirm the predictions of our theory

    Correction for the dislocation of curved surfaces caused by the PSF in 2D and 3D CT images

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    Conventional edge-detection methods suffer from the dislocation of curved surfaces due to the PSF. We propose a new method that uses the isophote curvature to circumvent this. It is accurate for objects with locally constant curvature, even for small objects (like blood vessels) and in the presence of noise

    Correction for the dislocation of curved surfaces caused by the PSF in 2D and 3D CT images

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    Conventional edge-detection methods suffer from the dislocation of curved surfaces due to the PSF. We propose a new method that uses the isophote curvature to circumvent this. It is accurate for objects with locally constant curvature, even for small objects (like blood vessels) and in the presence of noise

    Correction for the dislocation of curved surfaces caused by the PSF in 2D and 3D CT images

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    ๋ฌผ์งˆ ํ˜ผํ•ฉ๋น„์œจ๊ณผ ๊ตฌ์กฐ์  ํŠน์ง•์˜ ํ†ตํ•ฉ ์žฌ๊ตฌ์„ฑ ๋ชจ๋ธ์„ ์ด์šฉํ•œ ์ „์ž์  ์žฅ์„ธ์ฒ™ ๊ธฐ๋ฒ•

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์ „๊ธฐยท์ปดํ“จํ„ฐ๊ณตํ•™๋ถ€, 2013. 8. ์‹ ์˜๊ธธ.๋Œ€์žฅ ์ปดํ“จํ„ฐ ๋‹จ์ธต ์ดฌ์˜ ์˜์ƒ์—์„œ ์กฐ์˜ ์ฒ˜๋ฆฌ๋œ ์ž”์—ฌ๋ฌผ์„ ์ œ๊ฑฐํ•˜๊ธฐ ์œ„ํ•ด ์ „์ž์  ์žฅ์„ธ์ฒ™ ๋ฐฉ๋ฒ•์ด ์ด์šฉ๋œ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ์ „์ž์  ์žฅ์„ธ์ฒ™ ๋ฐฉ๋ฒ•์—์„œ ๊ฒฐํ•จ์˜ ์ฃผ์š” ์›์ธ์ด ๋˜๋Š” ๋ถ€๋ถ„ ์šฉ์  ํšจ๊ณผ์™€ ๊ฐ€์„ฑ ์ƒ์Šน ํšจ๊ณผ๋ฅผ ๋™์‹œ์— ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•ด ๋ฌผ์งˆ ํ˜ผํ•ฉ๋น„์œจ๊ณผ ๊ตฌ์กฐ์  ํŠน์ง•์˜ ํ†ตํ•ฉ ์žฌ๊ตฌ์„ฑ ๋ชจ๋ธ์„ ์ด์šฉํ•œ ์ „์ž์  ์žฅ์ฒญ์†Œ ๊ธฐ๋ฒ•์„ ์ œ์•ˆํ•œ๋‹ค. ๋จผ์ € ๋Œ€์žฅ ์ปดํ“จํ„ฐ ๋‹จ์ธต ์ดฌ์˜ ์˜์ƒ์—์„œ ๊ณต๊ธฐ, ์กฐ์˜ ์ฒ˜๋ฆฌ๋œ ์ž”์—ฌ๋ฌผ, ๊ณต๊ธฐ์™€ ์กฐ์˜ ์ฒ˜๋ฆฌ๋œ ์ž”์—ฌ๋ฌผ ์‚ฌ์ด์˜ ๊ฒฝ๊ณ„ (๊ณต๊ธฐ-์ž”์—ฌ๋ฌผ ๊ฒฝ๊ณ„), ๋Œ€์žฅ์™ธ๋ถ€์˜ ์—ฐ์กฐ์ง๊ณผ ์กฐ์˜ ์ฒ˜๋ฆฌ๋œ ์ž”์—ฌ๋ฌผ ์‚ฌ์ด์˜ ๊ฒฝ๊ณ„ (์—ฐ์กฐ์ง-์ž”์—ฌ๋ฌผ ๊ฒฝ๊ณ„), ๊ทธ๋ฆฌ๊ณ  ๊ณต๊ธฐ, ์—ฐ์กฐ์ง, ์กฐ์˜ ์ฒ˜๋ฆฌ๋œ ์ž”์—ฌ๋ฌผ์ด ๋งŒ๋‚˜๋Š” ๊ฒฝ๊ณ„ (๊ณต๊ธฐ-์—ฐ์กฐ์ง-์ž”์—ฌ๋ฌผ ๊ฒฝ๊ณ„) ์˜์—ญ์„ ํฌํ•จํ•œ ๊ฒฐ์žฅ ์š”์†Œ๋ฅผ ๋ถ„ํ• ํ•œ๋‹ค. ๋ถ„ํ• ๋œ ๊ณต๊ธฐ์™€ ๊ณต๊ธฐ-์ž”์—ฌ๋ฌผ ๊ฒฝ๊ณ„ ์˜์—ญ์— ๋Œ€ํ•ด์„œ๋Š” ๊ฐ ๋ณต์…€์˜ ๋ฐ€๋„๊ฐ’์„ ๋™์ผํ•˜๊ฒŒ ๊ณต๊ธฐ์˜ ๋Œ€ํ‘œ ๋ฐ€๋„๊ฐ’์œผ๋กœ ๋Œ€์ฒดํ•จ์œผ๋กœ์จ ์ž”์—ฌ๋ฌผ์„ ์ œ๊ฑฐํ•œ๋‹ค. ๋ฐ˜๋ฉด์— ๋ถ„ํ• ๋œ ์—ฐ์กฐ์ง-์ž”์—ฌ๋ฌผ ๊ฒฝ๊ณ„์™€ ๊ณต๊ธฐ-์—ฐ์กฐ์ง-์ž”์—ฌ๋ฌผ ๊ฒฝ๊ณ„ ์˜์—ญ์— ๋Œ€ํ•ด์„œ๋Š” ๋ฌผ์งˆ ํ˜ผํ•ฉ๋น„์œจ๊ณผ ๊ตฌ์กฐ์  ํŠน์ง•์„ ๊ณ„์‚ฐํ•œ๋‹ค. ๋ฌผ์งˆ ํ˜ผํ•ฉ๋น„์œจ์€ ๋‘ ๋ฌผ์งˆ๊ฐ„ ํ˜น์€ ์„ธ ๋ฌผ์งˆ๊ฐ„ ์ „์ด ๋ชจ๋ธ์„ ์ด์šฉํ•˜์—ฌ ์˜ˆ์ธกํ•˜๊ณ  ๊ตฌ์กฐ์  ํŠน์ง•์€ ํ—ค์‹œ์•ˆ ํ–‰๋ ฌ์˜ ์•„์ด๊ฒ ๋ถ„์„์— ๊ธฐ๋ฐ˜ํ•˜์—ฌ ๊ณ„์‚ฐํ•œ๋‹ค. ๊ณ„์‚ฐ๋œ ๋ฌผ์งˆ ํ˜ผํ•ฉ๋น„์œจ๊ณผ ๊ตฌ์กฐ์  ํŠน์ง•์„ ์ด์šฉํ•˜์—ฌ ์—ฐ์กฐ์ง-์ž”์—ฌ๋ฌผ ๊ฒฝ๊ณ„์™€ ๊ณต๊ธฐ-์—ฐ์กฐ์ง-์ž”์—ฌ๋ฌผ ๊ฒฝ๊ณ„ ์˜์—ญ์— ์†ํ•˜๋Š” ๊ฐ ๋ณต์…€์˜ ๋ฐ€๋„๊ฐ’์ด ์žฌ๊ตฌ์„ฑ๋œ๋‹ค. ๋ฌผ์งˆ ํ˜ผํ•ฉ๋น„์œจ๊ณผ ๊ตฌ์กฐ์  ํŠน์ง•์˜ ํ†ตํ•ฉ ์žฌ๊ตฌ์„ฑ ๋ชจ๋ธ์€ ๊ฐ ๋ณต์…€ ๋‚ด์˜ ์—ฐ์กฐ์ง์˜ ๋ถ€๋ถ„ ์šฉ์ ์„ ์œ ์ง€์‹œํ‚ค๋Š” ๋™์‹œ์— ์กฐ์˜ ์ฒ˜๋ฆฌ๋œ ์ž”์—ฌ๋ฌผ์˜ ๊ฐ€์„ฑ ์ƒ์Šน ํšจ๊ณผ๋กœ ์ธํ•ด ์•ฝํ™”๋œ ์ž”์—ฌ๋ฌผ์— ์ž ๊ธด ๋Œ€์žฅ ์ฃผ๋ฆ„ ๋ฐ ์šฉ์ข…์ด ๋ณด์กด๋  ์ˆ˜ ์žˆ๋„๋ก ํ•œ๋‹ค. ๋”ฐ๋ผ์„œ ์ œ์•ˆ๋œ ์ „์ž์  ์žฅ์„ธ์ฒ™ ๋ฐฉ๋ฒ•์—์„œ๋Š” ๋ถ€๋ถ„ ์šฉ์  ํšจ๊ณผ๋กœ ์ธํ•œ ์—ฐ์กฐ์ง-์ž”์—ฌ๋ฌผ ๊ฒฝ๊ณ„์˜ ๊ณ„๋‹จ๋ฌด๋Šฌ ๊ฒฐํ•จ๊ณผ ๊ฐ€์„ฑ ์ƒ์Šน ํšจ๊ณผ๋กœ ์ธํ•œ ์ž”์—ฌ๋ฌผ์— ์ž ๊ธด ๋Œ€์žฅ ์ฃผ๋ฆ„ ๋ฐ ์šฉ์ข…์˜ ์ง€๋‚˜์นœ ์„ธ์ฒ™ ๊ฒฐํ•จ์„ ํ”ผํ•  ์ˆ˜ ์žˆ๋‹ค. ๋˜ํ•œ ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ๊ธฐ์กด ์„ธ ๋ฌผ์งˆ๊ฐ„ ์ „์ด ๋ชจ๋ธ์˜ ์—ฐ์‚ฐ ๋ณต์žก๋„๋ฅผ ์ค„์ด๊ธฐ ์œ„ํ•ด ๋‹จ์ˆœ ์„ธ ๋ฌผ์งˆ๊ฐ„ ์ „์ด ๋ชจ๋ธ์„ ์ œ์•ˆํ•œ๋‹ค. ๋‹จ์ˆœ ์„ธ ๋ฌผ์งˆ๊ฐ„ ์ „์ด ๋ชจ๋ธ์—์„œ๋Š” ๋‘ ๋ฌผ์งˆ๊ฐ„ ์ „์ด ๋ชจ๋ธ์„ ๋ฐ˜๋ณต ์ ์šฉ์‹œํ‚ด์œผ๋กœ์จ ์–ป์–ด์ง„ ์„ธ ์Œ์˜ (๊ณต๊ธฐ-์—ฐ์กฐ์ง, ๊ณต๊ธฐ-์ž”์—ฌ๋ฌผ, ์—ฐ์กฐ์ง-์ž”์—ฌ๋ฌผ) ๋‘ ๋ฌผ์งˆ๊ฐ„ ํ˜ผํ•ฉ๋น„์œจ์„ ๊ตฌํ•˜๊ณ  ์ด๋ฅผ ์‚ผ๊ฐํ˜•์„ ์ด์šฉํ•œ ๋ฌด๊ฒŒ์ค‘์‹ฌ์ขŒํ‘œ ์ƒ์—์„œ์˜ ๋ณด๊ฐ„๋ฐฉ๋ฒ•์„ ์ด์šฉํ•ด ํ•˜๋‚˜์˜ ์„ธ ๋ฌผ์งˆ๊ฐ„ ํ˜ผํ•ฉ๋น„์œจ๋กœ ๋ณ€ํ™˜ํ•œ๋‹ค. ์—ด๊ฐœ์˜ ์ž„์ƒ ๋ฐ์ดํ„ฐ๋ฅผ ์ด์šฉํ•˜์—ฌ ์ œ์•ˆํ•œ ์ „์ž์  ์žฅ์„ธ์ฒ™ ๋ฐฉ๋ฒ•์˜ ์„ฑ๋Šฅ์„ ํ‰๊ฐ€ํ•˜์˜€๋‹ค. ๋ฐฉ์‚ฌ์„  ์ „๋ฌธ์˜์— ์˜ํ•œ ์žฅ์„ธ์ฒ™ ํ’ˆ์งˆ ํ‰๊ฐ€์—์„œ ์ œ์•ˆ ๋ฐฉ๋ฒ•์ด ๋ฌผ์งˆ ํ˜ผํ•ฉ๋น„์œจ์„ ์ด์šฉํ•œ ๊ธฐ์กด ๋ฐฉ๋ฒ•์— ๋น„ํ•ด ๋” ๋†’์€ ์ ์ˆ˜์˜ ์žฅ์„ธ์ฒ™ ๊ฒฐ๊ณผ๋ฅผ ๋ณด์˜€์œผ๋ฉฐ, ํŠนํžˆ ์ž”์—ฌ๋ฌผ์— ์ž ๊ธด ๋Œ€์žฅ ์ฃผ๋ฆ„ ๋ฐ ์šฉ์ข…์ด ๋” ์ž˜ ๋ณด์กด๋˜๋Š” ๊ฒƒ์„ ํ™•์ธํ•˜์˜€๋‹ค. ์ด๋Ÿฌํ•œ ๊ฒฐ๊ณผ๋Š” ์ž”์—ฌ๋ฌผ์— ์ž ๊ธด ๋Œ€์žฅ ์ฃผ๋ฆ„ ์˜์—ญ์„ ์ˆ˜๋™ ๋ถ„ํ• ํ•˜์—ฌ ์ œ์•ˆ ๋ฐฉ๋ฒ•๊ณผ ๊ธฐ์กด ๋ฐฉ๋ฒ•์— ์˜ํ•œ ์žฅ์„ธ์ฒ™ ๊ฒฐ๊ณผ ์˜์ƒ์—์„œ ํ•ด๋‹น ์˜์—ญ์˜ ํ‰๊ท  ๋ฐ€๋„๊ฐ’๊ณผ ์ฃผ๋ฆ„ ๋ณด์กด ๋น„์œจ์„ ๋น„๊ตํ•œ ๊ฒฐ๊ณผ์—์„œ๋„ ๋งˆ์ฐฌ๊ฐ€์ง€๋กœ ์ž…์ฆ๋˜์—ˆ๋‹ค. ๋˜ํ•œ ๊ธฐ์กด์˜ ๋‘ ๋ฌผ์งˆ๊ฐ„ ์ „์ด ๋ชจ๋ธ๋กœ๋Š” ์ž˜ ํ•ด๊ฒฐ๋˜์ง€ ์•Š์•˜๋˜ ๊ณต๊ธฐ-์—ฐ์กฐ์ง-์ž”์—ฌ๋ฌผ ๊ฒฝ๊ณ„ ์˜์—ญ์—์„œ์˜ ์‚ฐ๋“ฑ์„ฑ์ด ํ˜•ํƒœ์˜ ๊ฒฐํ•จ์— ๋Œ€ํ•ด์„œ๋„ ์ œ์•ˆ ๋ฐฉ๋ฒ•์—์„œ๋Š” ๋‹จ์ˆœ ์„ธ ๋ฌผ์งˆ๊ฐ„ ์ „์ด ๋ชจ๋ธ์„ ์ด์šฉํ•˜์—ฌ ๊ณต๊ธฐ-์—ฐ์กฐ์ง-์ž”์—ฌ๋ฌผ ๊ฒฝ๊ณ„ ์˜์—ญ์—์„œ์˜ ๊ฒฐํ•จ์„ ์ œ๊ฑฐํ•˜๊ณ  ์ „์ฒด ๋Œ€์žฅ์˜ ํ‘œ๋ฉด์ด ๊นจ๋—ํ•˜๊ฒŒ ์žฌ๊ตฌ์„ฑ๋˜๋Š” ๊ฒƒ์„ ํ™•์ธํ•˜์˜€๋‹ค.Electronic cleansing (EC) is the process of virtually cleansing the colon by removal of the tagged materials (TMs) in computed tomographic colonography (CTC) images and generating electronically cleansed images. We propose an EC method using a novel reconstruction model. To mitigate partial volume (PV) and pseudo-enhancement (PEH) effects simultaneously, material fractions and structural responses are integrated into a single reconstruction model. In our approach, colonic components including air, TM, interface layer between air and TM (air-TM interface) and interface layer between soft-tissue (ST) and TM (ST-TM interface), and T-junction (i.e., locations where air-TM interface with the colon wall) are first segmented. For each voxel in the segmented TM and air-TM interface, CT density value is replaced with the pure material density of air and thus the unexpected ST-like layers at the air-TM interface (caused by PV effect) are simply removed. On the other hand, for each voxel in the segmented ST-TM interface and T-junction, the two- and three-material fractions at the voxel are derived using a two- and three-material transition models, respectively. For each voxel in the segmented ST-TM interface and T-junction, the structural response is also calculated by rut- and cup-enhancement functions based on the eigenvalue signatures of the Hessian matrix. Then, CT density value of each voxel in ST-TM interface and T-junction is reconstructed based on both the material fractions and structural responses to conserve the PV contributions of ST in the voxel and preserve the folds and polyps submerged in TMs. Therefore, in our ST-preserving reconstruction model, the material fractions remove the aliasing artifacts at the ST-TM interface (caused by PV effect) effectively while the structural responses avoid the erroneous cleansing of the submerged folds and polyps (caused by PEH effect). To reduce the computational complexity of solving the orthogonal projection problem in the three-material model, we currently propose a new projection method for the three-material model that provides a very quick estimate of the three-material fractions without the use of code-book, which is pre-generated by uniformly sampling the model representation in material fraction space and used to find the best match with the observed measurements. In our new projection method for the three-material model, three pairs of two-material fractions are calculated by using the two-material model and then simply combined into a single triple of three-material fractions based on the barycentric interpolation in material fraction space. Experimental results using clinical datasets demonstrated that the proposed EC method showed higher cleansing quality and better preservation of submerged folds and polyps than the previous method. In addition, by using the new projection method for the three-material model, the proposed EC method clearly reconstructed the whole colon surface without the T-junction artifacts, which are observed as distracting ridges along the line where the air-TM interface touches the colon surface when the two-material model does not cope with the three-material fractions at T-junctions.Docto
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