國立虎尾科技大學 |

Circular Probabilistic Based Color Processing : = Applications in Digital Pathology Image Analysis.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Circular Probabilistic Based Color Processing :/
其他題名:	Applications in Digital Pathology Image Analysis.
作者:	Li, Xingyu.
面頁冊數:	1 online resource (184 pages)
附註:	Source: Dissertation Abstracts International, Volume: 79-04(E), Section: B.
Contained By:	Dissertation Abstracts International79-04B(E).
標題:	Electrical engineering. -
電子資源:	click for full text (PQDT)
ISBN:	9780355531152

Circular Probabilistic Based Color Processing : = Applications in Digital Pathology Image Analysis.
Li, Xingyu.

Circular Probabilistic Based Color Processing :Applications in Digital Pathology Image Analysis. - 1 online resource (184 pages)

Source: Dissertation Abstracts International, Volume: 79-04(E), Section: B.

Thesis (Ph.D.)

Includes bibliographical references

Cancer is the leading cause of death in Canada. As the only definitive diagnosis for cancer, pathology confronts more challenges with the severe social issue of aging population. Past decade has witnessed the advent of whole-slide imaging, which fosters the research of digital pathology image understanding and automatic cancer diagnosis to address challenges in pathology.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780355531152Subjects--Topical Terms:

596380
Electrical engineering.
Index Terms--Genre/Form:

554714
Electronic books.

Circular Probabilistic Based Color Processing : = Applications in Digital Pathology Image Analysis.
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245 1 0 $a Circular Probabilistic Based Color Processing : $b Applications in Digital Pathology Image Analysis.
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300 $a 1 online resource (184 pages)
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500 $a Source: Dissertation Abstracts International, Volume: 79-04(E), Section: B.
500 $a Adviser: Konstantinos N. Plataniotis.
502 $a Thesis (Ph.D.) $c University of Toronto (Canada) $d 2017.
504 $a Includes bibliographical references
520 $a Cancer is the leading cause of death in Canada. As the only definitive diagnosis for cancer, pathology confronts more challenges with the severe social issue of aging population. Past decade has witnessed the advent of whole-slide imaging, which fosters the research of digital pathology image understanding and automatic cancer diagnosis to address challenges in pathology.
520 $a Color plays a vital role in digital pathology image analysis due to the use of chemical staining in pathology examination. However, unclear color mixing due to histological substances' co-localization and color variation among pathology images introduced by the inconsistency in pathology image preparation make reliable color-based quantitative pathology image analysis challenging. To overcome these problems, this research investigates the unique imaging model of pathology images, and introduces pathology image centered color processing algorithms based on two novel color signal treatments in the cylindrical color domain: circular probabilistic color-based pixel clustering and saturation-weighted color statistics. In the first treatment, aware of the directional nature of hue, the study innovates to model a hue distribution of an image using a circular mixture distribution, and provides a complete hue-based pixel clustering solution through maximum likelihood estimation. The second method aims to address the singularity of the HSV space in color processing. Motivated by the close relationship between saturation and hue in color perception, saturation-weighted statistics is generalized to mitigate effects of achromatic pixels on color analysis. The proposed two color treatments benefit the understanding of color content in an image.
520 $a Based on the proposed color treatments, image-dependent color estimation, blind stain decomposition for color unmixing, and a complete color normalization scheme are proposed in this dissertation. The color estimation pipeline computes the representative color of histological substances in a pathology image, building an adaptive mapping between image color and tissue content. Toward accurate stain decomposition, taking color cues obtained from the novel color treatments and the problem's physical constraint into consideration, the introduced method obtains an optimal stain separation with minimal decomposition residue in an iterative manner. In the pathology image centered color normalization scheme, by implicitly distinguishing causes of color variation, illuminant normalization and stain spectral normalization are cascaded. Extensive experimentation suggests that the introduced solutions are superior to prior arts in terms of robustness to achromatic noise, effectiveness, and capability for histological information preservation.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Electrical engineering. $3 596380
655 7 $a Electronic books. $2 local $3 554714
690 $a 0544
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a University of Toronto (Canada). $b Electrical and Computer Engineering. $3 1148628
773 0 $t Dissertation Abstracts International $g 79-04B(E).
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10605892 $z click for full text (PQDT)