Automatic determination of mass functions in Dempster-Shafer theory using fuzzy c-means and spatial neighborhood information for image segmentation

2002 ◽  
Vol 41 (4) ◽  
pp. 760 ◽  
Author(s):  
Yue Min Zhu
Keyword(s):  
Image Segmentation ◽  
Automatic Determination ◽  
Fuzzy C Means ◽  
Neighborhood Information ◽  
Dempster Shafer Theory ◽  
Shafer Theory ◽  
Mass Functions

A new method based on Dempster–Shafer theory and fuzzy c-means for brain MRI segmentation

2015 ◽  
Vol 26 (10) ◽  
pp. 105402 ◽  
Author(s):  
Jie Liu ◽  
Xi Lu ◽  
Yunpeng Li ◽  
Xiaowu Chen ◽  
Yong Deng
Keyword(s):  
Brain Mri ◽  
New Method ◽  
Mri Segmentation ◽  
Fuzzy C Means ◽  
Dempster Shafer Theory ◽  
Shafer Theory

scholarly journals AUTOMATIC DETERMINATION OF SEEDS FOR RANDOM WALKER BY SEEDED WATERSHED TRANSFORM FOR TUNA IMAGE SEGMENTATION

2018 ◽  
Vol 11 (1) ◽  
pp. 52 ◽  
Author(s):  
Moch Zawaruddin Abdullah ◽  
Dinial Utami Nurul Qomariah ◽  
Lafnidita Farosanti ◽  
Agus Zainal Arifin
Keyword(s):  
Image Segmentation ◽  
Image Classification ◽  
Misclassification Error ◽  
Segmentation Method ◽  
Automatic Determination ◽  
Tuna Fish ◽  
Watershed Transform ◽  
Random Walker

Tuna fish image classification is an important part to sort out the type and quality of the tuna based upon the shape. The image of tuna should have good segmentation results before entering the classification stage. It has uneven lighting and complex texture resulting in inappropriate segmentation. This research proposed method of automatic determination seeded random walker in the watershed region for tuna image segmentation. Random walker is a noise-resistant segmentation method that requires two types of seeds defined by the user, the seed pixels for background and seed pixels for the object. We evaluated the proposed method on 30 images of tuna using relative foreground area error (RAE), misclassification error (ME), and modified Hausdroff distances (MHD) evaluation methods with values of 4.38%, 1.34% and 1.11%, respectively. This suggests that the seeded random walker method is more effective than exiting methods for tuna image segmentation.

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