Second order DTMR image segmentation using random walker

El-Hilo, Saba; Weldeselassie, Yonas T.; Atkins, M. Stella

doi:10.1117/12.878216

Cited by 1 publication

(1 citation statement)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Instead of manually identifying and labeling the seed pixels of foreground and background, research on automating the seeding process has been addressed in [2][3][4]. Besides that, several studies have also been made to incorporate an extra term particularly the prior knowledge into the random walks algorithm so as to automate and enhance the results of random walks algorithm [5][6][7]. In these studies, the 'aspatial' energy term obtained from the training images or input from the user was integrated into either the conventional spatial energy function or the distance function in the random walks.…”

Section: Introductionmentioning

confidence: 99%

Random walker with improved weighting function for interactive medical image segmentation

Yin

Rajeswari

2014

Bio-Medical Materials and Engineering

View full text Add to dashboard Cite

To segment an image using the random walks algorithm; users are often required to initialize the approximate locations of the objects and background in the image. Due to its segmenting model that is mainly reflected by the relationship among the neighborhood pixels and its boundary conditions, random walks algorithm has made itself sensitive to the inputs of the seeds. Instead of considering the relationship between the neighborhood pixels solely, an attempt has been made to modify the weighting function that accounts for the intensity changes between the neighborhood nodes. Local affiliation within the defined neighborhood region of the two nodes is taken into consideration by incorporating an extra penalty term into the weighting function. Besides that, to better segment images, particularly medical images with texture features, GLCM variance is incorporated into the weighting function through kernel density estimation (KDE). The probability density of each pixel belonging to the initialized seeds is estimated and integrated into the weighting function. To test the performance of the proposed weighting model, several medical images that mainly made up of 174-brain tumor images are experimented. These experiments establish that the proposed method produces better segmentation results than the original random walks.

show abstract