A dynamic threshold edge-preserving smoothing segmentation algorithm for anterior chamber OCT images based on modified histogram

2011 ◽  
Author(s):  
Wenliang Du ◽  
Xiaolin Tian ◽  
Yankui Sun
Keyword(s):  
Anterior Chamber ◽  
Segmentation Algorithm ◽  
Dynamic Threshold ◽  
Edge Preserving

Anisotropic Dynamic-Morphological-Diffusion for Segmentation of Noisy Color Images

2014 ◽  
Vol 18 (2) ◽  
pp. 204-212
Author(s):  
Junji Maeda ◽  
◽  
Takehiro Harada ◽  
Sato Saga ◽  
Yukinori Suzuki ◽  
...  
Keyword(s):  
Anisotropic Diffusion ◽  
Color Images ◽  
Segmentation Algorithm ◽  
Morphological Filter ◽  
Smoothing Technique ◽  
Edge Preserving ◽  
Dynamic Selection ◽  
Small Image ◽  
Selection Of

In this paper, we propose a modified anisotropic diffusion algorithm using the dynamicmorphological filtering as a new precise edge-preserving smoothing technique for an accurate segmentation of color images with heavy noise. We incorporate a dynamic selection of multiple structuring elements for each pixel in the morphological filter before the anisotropic diffusion in order to improve the ability of edge-preserving smoothing. As a result, noise and unnecessary details of images are effectively smoothed while preserving small image structures before performing a segmentation algorithm. Several simulated examples are presented that demonstrate the effectiveness of the proposed technique.

The Segmentation Algorithm for Hand Vein Images Based on Improved Niback Algorithm

2012 ◽  
Vol 532-533 ◽  
pp. 1558-1562 ◽  
Author(s):  
Xiu Mei Guo ◽  
Wei Dong Zhou ◽  
Cheng Yi Wang
Keyword(s):  
Feature Extraction ◽  
Segmentation Algorithm ◽  
Dynamic Threshold ◽  
Local Dynamic ◽  
Threshold Segmentation ◽  
Hand Vein ◽  
The Common ◽  
Improved Algorithm

The local dynamic threshold segmentation algorithm, Niblack algorithm, is more suitable for hand vein images segmentation by comparing the common- used algorithms. But the traditional Niblack algorithm has weakness, so this paper proposed an improved Niblack algorithm in which the coefficient is adaptive estimated. The results show that the improved algorithm can better segment the hand vein outlines, preserve the vein original characteristics, and meet the needs of the follow-up feature extraction and recognition.

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