Two graph searching techniques for boundary finding in white blood cell images

Lester, James M.; Williams, H.A.; Weintraub, Bernard; Brenner, John F.

doi:10.1016/0010-4825(78)90030-6

Cited by 71 publications

(11 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To enhance the latter, only appropriate connected edges have to be selected from the edge map produced by the detector. This can be done, for instance, by assigning weights to each detected candidate edge and using graph search techniques [77,78] or dynamic programming [79] to find optimal connected edges minimizing a certain cost function.…”

Section: Sobel Edge Detector Initial Imagementioning

confidence: 99%

Medical Image Segmentation: A Brief Survey

Elnakib¹,

Gimel’farb²,

Suri³

et al. 2011

Multi Modality State-of-the-Art Medical Image Segmentation and Registration Methodologies

View full text Add to dashboard Cite

Accurate segmentation of 2-D, 3-D, and 4-D medical images to isolate anatomical objects of interest for analysis is essential in almost any computer-aided diagnosis system or other medical imaging applications. Various aspects of segmentation features and algorithms have been extensively explored for many years in a host of publications. However, the problem remains challenging, with no general and unique solution, due to a large and constantly growing number of different objects of interest, large variations of their properties in images, different medical imaging modalities, and associated changes of signal homogeneity, variability, and noise for each object. This chapter overviews most popular medical image segmentation techniques and discusses their capabilities, and basic advantages and limitations. The state-of-the-art techniques of the last decade are also outlined.

show abstract

Section: Sobel Edge Detector Initial Imagementioning

confidence: 99%

Medical Image Segmentation: A Brief Survey

Elnakib¹,

Gimel’farb²,

Suri³

et al. 2011

Multi Modality State-of-the-Art Medical Image Segmentation and Registration Methodologies

View full text Add to dashboard Cite

show abstract

“…This edge quality function may, for example, be defined to be the average of the gradient magnitudes of the points, minus some measure of their average disagreement in orientation angles [35,36]. This edge quality function may, for example, be defined to be the average of the gradient magnitudes of the points, minus some measure of their average disagreement in orientation angles [35,36].…”

Section: T H E R O B E R T S E D G E D E T E C T O Rmentioning

confidence: 99%

Image Segmentation

Wu¹,

Kenneth²

2008

Microscope Image Processing

View full text Add to dashboard Cite

“…The selection of the next node in the OPEN list can be made with a depth-first strategy or using a rating function [3], [10], [11]. Lester and d suggest to take the maximum cost arc of the path as the value of g. The advantage is that g does not build up continuously with depth, so that good paths can be followed for a long time [5]. Since the value of g necessary increases with depth if the costs are positive, Ashkar and Modestino proposed a cost function that takes negative values if the edge has a good evaluation [11. However, if some interesting ideas have been proposed, it seems that it is not possible to keep looking for the path with minimum cost and at the same time to have a depth first strategy, avoiding small undesirable paths.…”

Section: Choice Of Heuristicsmentioning

confidence: 99%