Intuitionistic fuzzy approach for enhancement of low contrast mammogram images

2020 ◽  
Vol 30 (4) ◽  
pp. 1162-1172
Author(s):  
Tamalika Chaira
Author(s):  
Jayanthi Kuppannan ◽  
Parvathi Rangasamy ◽  
Devi Thirupathi ◽  
N. Palaniappan

2016 ◽  
Vol 49 ◽  
pp. 238-247 ◽  
Author(s):  
Hossein Sayyadi Tooranloo ◽  
Arezoo sadat Ayatollah

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Dejian Yu

We establish a decision making model for evaluating hydrogen production technologies in China, based on interval-valued intuitionistic fuzzy set theory. First of all, we propose a series of interaction interval-valued intuitionistic fuzzy aggregation operators comparing them with some widely used and cited aggregation operators. In particular, we focus on the key issue of the relationships between the proposed operators and existing operators for clear understanding of the motivation for proposing these interaction operators. This research then studies a group decision making method for determining the best hydrogen production technologies using interval-valued intuitionistic fuzzy approach. The research results of this paper are more scientific for two reasons. First, the interval-valued intuitionistic fuzzy approach applied in this paper is more suitable than other approaches regarding the expression of the decision maker’s preference information. Second, the results are obtained by the interaction between the membership degree interval and the nonmembership degree interval. Additionally, we apply this approach to evaluate the hydrogen production technologies in China and compare it with other methods.


2022 ◽  
pp. 340-349
Author(s):  
Alankrita Aggarwal ◽  
Deepak Chatha

An artificial neural network (ANN) is used to resolve problems related to complex scenarios and logical thinking. Nowadays, a cause for concern is the mortality rate among women due to cancer. Generally, women to around 45 years old are the most vulnerable to this disease. Early detection is the only hope for the patient to survive, otherwise it may reach an unrecoverable stage. Currently, there are numerous techniques available for the diagnosis of such diseases out of which mammography is the most trustworthy method for detecting early stage cancer. The analysis of these mammogram images is always difficult to analyze due to low contrast and non-uniform background. The mammogram images are scanned, digitized for processing, nut that further reduces the contrast between region of interest (ROI) and the background. Furthermore, presence of noise, glands, and muscles leads to background contrast variations. The boundaries of the suspected tumor area are always fuzzy and improper. The aim of this article is to develop a robust edge detection technique which works optimally on mammogram images to segment a tumor area.


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