Vein Image Segmentation Based on One-Dimentional Gray and Filter Erosion Method

2014 ◽  
Vol 635-637 ◽  
pp. 1049-1055 ◽  
Author(s):  
Xun Zhang ◽  
Yong Hong Guo ◽  
Gang Li ◽  
Jin Long He
Keyword(s):  
Image Segmentation ◽  
Test Results ◽  
Segmentation Method ◽  
Low Contrast ◽  
One Dimensional ◽  
Hand Vein ◽  
Binary Morphology

For the low contrast and serious noises, a fast image segmentation method based on one-dimensional gray segmentation, binary morphology erosion and area elimination is proposed. Since veins are thin and long, the vein image can be easily distinguished from background by judging the gray difference from nearby pixels when they are vertically or horizontally scanned. Then the processed image is diposed with erosion and area elimination to filter the noise. According to test results on the hand vein images which got from the equipment constructed by ourselves, it is proved that the method is more suitable for hand vein image segmentation than others and clear vein images can be botained quickly.

An Image Segmentation Method Based on Adaptability Threshold

2013 ◽  
Vol 734-737 ◽  
pp. 2912-2916
Author(s):  
Hui Li ◽  
Ping He
Keyword(s):  
Image Segmentation ◽  
Strain Measurement ◽  
Maximum Entropy Method ◽  
Entropy Method ◽  
Important Application ◽  
Original Image ◽  
Segmentation Method ◽  
One Dimensional ◽  
The One ◽  
Application Fields

Automation strain measurement of the sheet metal deforming becomes one of the important application fields of computer vision. The algorithm of image segmentation based on adaptability threshold was presented for image segmentation of metal steel. In order to validate the proposed method, it is tested and compared with Ostu method and the one-dimensional maximum entropy method. Experiment results indicate that the method is simple and effective, and has an advantage of reservation of the main features of the original image.

Image Segmentation Method Based on Fisher Criterion and Genetic Algorithm

2011 ◽  
Vol 474-476 ◽  
pp. 928-932
Author(s):  
Xian Xiang Fu ◽  
Zu Jue Chen ◽  
Yong Fu Zhao
Keyword(s):  
Genetic Algorithm ◽  
Image Segmentation ◽  
Precision Agriculture ◽  
Automatic Segmentation ◽  
Adaptive Genetic Algorithm ◽  
Salt And Pepper Noise ◽  
Segmentation Method ◽  
Fisher Criterion ◽  
One Dimensional ◽  
Salt And Pepper

Precise recognition of the weed by computer vision, furthermore raising the weeding efficiency, reducing the use of herbicide, and decreasing the pollution to the environment is one of the key technologies in the field of precision agriculture. To determine the optimal threshold in image automatic segmentation and solve one-dimensional histogram without obvious peak and valley distribution, image segmentation method based on fisher criterion and improved adaptive genetic algorithm is proposed. This method can preserve the multifamily of population and the astringency of the algorithm, and can overcome the problems of poor astringency and premature occurrence. The result shows that the proposed approach has better immunity to Salt and Pepper Noise and greatly shortens the time of image segmentation.

An Effective X-Ray Image Segmentation Method for the Pharmaceutical Industry via Surface Fitting

2013 ◽  
Vol 333-335 ◽  
pp. 839-844
Author(s):  
Kai Hong Shi ◽  
Zong Qing Lu ◽  
Qing Min Liao
Keyword(s):  
Image Segmentation ◽  
Pharmaceutical Industry ◽  
Active Contour Model ◽  
Three Dimensional ◽  
Surface Fitting ◽  
Segmentation Method ◽  
Image Subtraction ◽  
Low Contrast ◽  
X Ray ◽  
Fixed Condition

Image segmentation techniques currently used for X-ray inspection in pharmaceutical industry suffer from some limitations. The object in an image is close to the background and its contours are weak or blurred because of the X-ray imaging characteristic. Based on our research of X-ray inspection, a simple and efficient image segmentation method is proposed in this paper. It is implemented by treating the image and desired contours as three dimensional surface and holes respectively in order to simplify the model of segmentation, and making use of surface fitting and image subtraction to extract the target region efficiently. The novelty of this approach is that we need less selection of parameters to extract contours with low contrast by surface fitting. Experiments on real X-ray images demonstrate the advantages of the proposed method over active contour model (ACM) and Chan_Vese model (CV model) in terms of both accuracy and efficiency on fixed condition.

scholarly journals Concrete Image Segmentation Based on Multiscale Mathematic Morphology Operators and Otsu Method

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Sheng-Bo Zhou ◽  
Ai-Qin Shen ◽  
Geng-Fei Li
Keyword(s):  
Computed Tomography ◽  
Image Segmentation ◽  
Noise Suppression ◽  
Segmentation Method ◽  
Low Contrast ◽  
Otsu Method ◽  
Edge Detectors ◽  
Mathematic Morphology ◽  
Threshold Algorithms ◽  
Concrete Image

The aim of the current study lies in the development of a reformative technique of image segmentation for Computed Tomography (CT) concrete images with the strength grades of C30 and C40. The results, through the comparison of the traditional threshold algorithms, indicate that three threshold algorithms and five edge detectors fail to meet the demand of segmentation for Computed Tomography concrete images. The paper proposes a new segmentation method, by combining multiscale noise suppression morphology edge detector with Otsu method, which is more appropriate for the segmentation of Computed Tomography concrete images with low contrast. This method cannot only locate the boundaries between objects and background with high accuracy, but also obtain a complete edge and eliminate noise.

AUTOMATIC BAND SEGMENTATION AND DETECTION OF 1D-GEL IMAGES WITH DIFFERENT EXPOSURES

2014 ◽  
Vol 28 (01) ◽  
pp. 1457001
Author(s):  
MENG-HSIUN TSAI ◽  
SHU-WEI GUO ◽  
YUNG-KUAN CHAN ◽  
JIUNN-LIN WU ◽  
CHING-LIN WANG ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Gel Electrophoresis ◽  
Poor Quality ◽  
Experimental Results ◽  
Segmentation Method ◽  
High Exposure ◽  
One Dimensional ◽  
Segmentation Methods ◽  
Low Exposure

Many gel electrophoresis image segmentation methods have been proposed. However, most of the proposed methods cannot provide a satisfying performance since a gel electrophoresis image is a manually captured image often with various noise and poor quality. This paper presents a one-dimensional-gel image segmentation method (1DGISM) to cut the bands off from a one-dimensional gel electrophoresis image (1D-gel image). The experimental results showed that 1DGISM can give an impressive segmentation performance even if the band boundaries are very indistinct. Moreover, the authors suggest that a low exposure for imaging be helpful in segmenting bright bands. Alternatively, taking the image at a high exposure is useful if the bands are dark.

Multiresolution Bio-Medical Image Segmentation using Fuzzy C-Means Clustering

2019 ◽  
Vol 8 (4) ◽  
pp. 9548-9551
Keyword(s):  
Image Segmentation ◽  
Medical Image Segmentation ◽  
Experimental Result ◽  
Segmentation Method ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering ◽  
Robust Image ◽  
Multiple Clusters ◽  
Single Pixel ◽  
Popular Image

Fuzzy c-means clustering is a popular image segmentation technique, in which a single pixel belongs to multiple clusters, with varying degree of membership. The main drawback of this method is it sensitive to noise. This method can be improved by incorporating multiresolution stationary wavelet analysis. In this paper we develop a robust image segmentation method using Fuzzy c-means clustering and wavelet transform. The experimental result shows that the proposed method is more accurate than the Fuzzy c-means clustering.

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