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Mathematical morphological processin...

Zhang, Xin.

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Mathematical morphological processing for retinal image analysis.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Mathematical morphological processing for retinal image analysis./
作者:	Zhang, Xin.
面頁冊數:	68 p.
附註:	Source: Masters Abstracts International, Volume: 44-01, page: 0501.
Contained By:	Masters Abstracts International44-01.
標題:	Engineering, Electronics and Electrical. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1427882
ISBN:	9780542216770

Mathematical morphological processing for retinal image analysis.
Zhang, Xin.

Mathematical morphological processing for retinal image analysis. - 68 p.

Source: Masters Abstracts International, Volume: 44-01, page: 0501.

Thesis (M.S.)--Oklahoma State University, 2005.

Scope and method of study. Diabetic retinopathy is the leading cause of the blindness in the western world. Digital retinal imaging with remote image evaluations is a promising new solution to accurately and precisely stage patients conveniently. The spot lesion detection is the primary step.

ISBN: 9780542216770Subjects--Topical Terms:

626636
Engineering, Electronics and Electrical.

Mathematical morphological processing for retinal image analysis.
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245 1 0 $a Mathematical morphological processing for retinal image analysis.
300 $a 68 p.
500 $a Source: Masters Abstracts International, Volume: 44-01, page: 0501.
500 $a Adviser: Guoliang Fan.
502 $a Thesis (M.S.)--Oklahoma State University, 2005.
520 $a Scope and method of study. Diabetic retinopathy is the leading cause of the blindness in the western world. Digital retinal imaging with remote image evaluations is a promising new solution to accurately and precisely stage patients conveniently. The spot lesion detection is the primary step.
520 $a Findings and conclusions. Based on the mathematical morphology, we discussed two lesion extraction algorithms. To avoid over-segmentation, inner and outer markers are introduced into the marker controlled watershed segmentation method. Gradient image is generated by multicolor channels. Marked lesions can be successfully extracted with clear boundaries. The second method, the adaptive multiscale morphological processing, is a novel procedure to efficiently extract spot lesions in the fundus image. The relative contrast of lesions with the surrounding background is used as criteria, which are similar to the human vision property. Entropy-based thresholding can well distinguish lesions. Post processing removes misclassified areas and produces vascular tree. Both algorithms have been tested in the Clemson University's database.
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