Adaptive striping watershed segmentation method for processing microscopic images of overlapping irregular-shaped and multicentre particles

2014 ◽  
Vol 258 (1) ◽  
pp. 6-12 ◽  
Author(s):  
X. XIAO ◽  
B. BAI ◽  
N. XU ◽  
K. WU
Keyword(s):  
Segmentation Method ◽  
Watershed Segmentation ◽  
Microscopic Images

scholarly journals Investigation of fragment sizes in laser-driven shock-loaded tin with improved watershed segmentation method

2014 ◽  
Vol 22 (16) ◽  
pp. 18924 ◽  
Author(s):  
Weihua He ◽  
Jianting Xin ◽  
Genbai Chu ◽  
Jing Li ◽  
Jianli Shao ◽  
...  
Keyword(s):  
Segmentation Method ◽  
Watershed Segmentation

A Watershed Segmentation Method with Shape-Based Merging Criterion Based on Multispectral Remote Sensing Imagery

2011 ◽  
Vol 9 (3) ◽  
pp. 1006-1013 ◽  
Author(s):  
Anzhi Yue ◽  
Jianyu Yang ◽  
Chao Zhang ◽  
Wei Su ◽  
Wenju Yun ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Segmentation Method ◽  
Watershed Segmentation ◽  
Remote Sensing Imagery ◽  
Multispectral Remote Sensing

A Modified Watershed Segmentation Method to Segment Renal Calculi in Ultrasound Kidney Images

2012 ◽  
Vol 8 (1) ◽  
pp. 46-61 ◽  
Author(s):  
P. R. Tamilselvi ◽  
P. Thangaraj
Keyword(s):  
Sensitivity And Specificity ◽  
Fuzzy Inference ◽  
Renal Calculi ◽  
Abdominal Ultrasound ◽  
Ultra Sound ◽  
Ultrasound Images ◽  
Segmentation Method ◽  
Watershed Segmentation ◽  
Inference System ◽  
Unique Challenge

Segmentation of stones from abdominal ultrasound images is a unique challenge to the researchers because these images have heavy speckle noise and attenuated artifacts. In the previous renal calculi segmentation method, the stones were segmented from the medical ultra sound kidney stone images using Adaptive Neuro Fuzzy Inference System (ANFIS). But, the method lacks in sensitivity and specificity measures. The segmentation method is inadequate in its performance in terms of these two parameters. So, to avoid these drawbacks, a new segmentation method is proposed in this paper. Here, new region indicators and new modified watershed transformation is utilized. The proposed method is comprised of four major processes, namely, preprocessing, determination of outer and inner region indictors, modified watershed segmentation with ANFIS performance. The method is implemented and the results are analyzed in terms of various statistical performance measures. The results show the effectiveness of proposed segmentation method in segmenting the kidney stones and the achieved improvement in sensitivity and specificity measures. Furthermore, the performance of the proposed technique is evaluated by comparing with the other segmentation methods.

Recognition Method of Cotton Blind Stinkbug Hazard Level Based on Image Processing

2014 ◽  
Vol 511-512 ◽  
pp. 481-489
Author(s):  
Jian Hua Zhang ◽  
Fan Tao Kong ◽  
Wei Chen ◽  
Jian Zhai Wu ◽  
Meng Shuai Zhu
Keyword(s):  
Cotton Leaf ◽  
Shape Features ◽  
Bayes Classifier ◽  
Segmentation Method ◽  
Watershed Segmentation ◽  
Recognition Method ◽  
Natural Conditions ◽  
Model Classification ◽  
Hazard Level ◽  
Hazard Rating

In view of complex background of cotton blind stinkbug hazard region and the difficulty in segmentation and classification under natural conditions, an automatic classification method of cotton blind stinkbug hazard level was proposed. In this method, crop regions and disease regions of cotton were extract respectively by H+a*+b* component and Otsu segmentation method based on blind stinkbug hazard cotton leaves. Adhesion cotton leaves separated by Watershed segmentation method and cotton leaf area hazard by blind stinkbug extracted. According to cotton blind the stinkbug hazard rating standard, combination Naive Bayes classifier and color, texture and shape features extracted from images to classify the hazard rating of the blind stinkbug. The results showed that the model classification correct rate was 90.0%, it could classify the hazard rating of the cotton blind stinkbug and provide technical support for the prevention and treatment of the cotton blind stinkbug.

An Improved Image Segmentation Method Based Onmulti-Resolution Analysis and Watershed Transformation

2012 ◽  
Vol 500 ◽  
pp. 709-715
Author(s):  
Yan Wang ◽  
Yan Ma
Keyword(s):  
Image Segmentation ◽  
Wavelet Transform ◽  
Input Image ◽  
Segmentation Method ◽  
Watershed Segmentation ◽  
Watershed Transformation ◽  
Inverse Transform ◽  
Multi Resolution Analysis ◽  
Segmentation Accuracy ◽  
Resolution Analysis

This paper presents an improved image segmentation method based on multi-resolution analysis of wavelet transform and watershed transformation. In the marked-controlled watershed segmentation, we not only enhance the contours of low-resolution input image to acquire segmentation function image, but also use minima imposition technology to apply filters to input image to acquire marked function image. In order to improve segmentation accuracy, we use regional fusion and coarse-fine segmentation in wavelet inverse transform. The experimental results show that the proposed image segmentation method can efficiently reduce over-segmentation, as well as improve the effect of image segmentation. In addition, the proposed method is robust.

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