Comparison of 3-point dixon imaging and fuzzy C-means clustering methods for breast density measurement

2013 ◽  
Vol 38 (2) ◽  
pp. 474-481 ◽  
Author(s):  
Tess V. Clendenen ◽  
Anne Zeleniuch-Jacquotte ◽  
Linda Moy ◽  
Malcolm C. Pike ◽  
Henry Rusinek ◽  
...  
Keyword(s):  
Breast Density ◽  
Density Measurement ◽  
Clustering Methods ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering ◽  
Dixon Imaging ◽  
Breast Density Measurement

Improving Image Search through MKFCM Clustering Strategy-Based Re-ranking Measure

2018 ◽  
Vol 29 (1) ◽  
pp. 497-514
Author(s):  
A.K. Naveena ◽  
N.K. Narayanan
Keyword(s):  
Clustering Algorithm ◽  
Data Retrieval ◽  
Initial Step ◽  
Discrete Wavelet ◽  
Image Search ◽  
Clustering Methods ◽  
Fuzzy C Means ◽  
Image Retrieval System ◽  
Fuzzy C Means Clustering ◽  
Search Approach

Abstract The main intention of this research is to develop a novel ranking measure for content-based image retrieval system. Owing to the achievement of data retrieval, most commercial search engines still utilize a text-based search approach for image search by utilizing encompassing textual information. As the text information is, in some cases, noisy and even inaccessible, the drawback of such a recovery strategy is to the extent that it cannot depict the contents of images precisely, subsequently hampering the execution of image search. In order to improve the performance of image search, we propose in this work a novel algorithm for improving image search through a multi-kernel fuzzy c-means (MKFCM) algorithm. In the initial step of our method, images are retrieved using four-level discrete wavelet transform-based features and the MKFCM clustering algorithm. Next, the retrieved images are analyzed using fuzzy c-means clustering methods, and the rank of the results is adjusted according to the distance of a cluster from a query. To improve the ranking performance, we combine the retrieved result and ranking result. At last, we obtain the ranked retrieved images. In addition, we analyze the effects of different clustering methods. The effectiveness of the proposed methodology is analyzed with the help of precision, recall, and F-measures.

TH-A-217BCD-07: Validation of Breast Density Measurement Using Dual Energy Mammography: A Post Mortem Study

2012 ◽  
Vol 39 (6Part29) ◽  
pp. 3990-3990
Author(s):  
J L Ducote ◽  
H Ding ◽  
M Klopfer ◽  
S Molloi
Keyword(s):  
Breast Density ◽  
Density Measurement ◽  
Dual Energy ◽  
Post Mortem ◽  
Breast Density Measurement

scholarly journals Fuzzy C-Means Clustering Algorithm with Multiple Fuzzification Coefficients

Algorithms ◽  
2020 ◽  
Vol 13 (7) ◽  
pp. 158
Author(s):  
Tran Dinh Khang ◽  
Nguyen Duc Vuong ◽  
Manh-Kien Tran ◽  
Michael Fowler
Keyword(s):  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Clustering Methods ◽  
Clustering Method ◽  
Machine Learning Technique ◽  
Practical Applications ◽  
Fuzzy C Means ◽  
Fuzzy Clustering Method ◽  
Learning Technique ◽  
Fuzzy C Means Clustering

Clustering is an unsupervised machine learning technique with many practical applications that has gathered extensive research interest. Aside from deterministic or probabilistic techniques, fuzzy C-means clustering (FCM) is also a common clustering technique. Since the advent of the FCM method, many improvements have been made to increase clustering efficiency. These improvements focus on adjusting the membership representation of elements in the clusters, or on fuzzifying and defuzzifying techniques, as well as the distance function between elements. This study proposes a novel fuzzy clustering algorithm using multiple different fuzzification coefficients depending on the characteristics of each data sample. The proposed fuzzy clustering method has similar calculation steps to FCM with some modifications. The formulas are derived to ensure convergence. The main contribution of this approach is the utilization of multiple fuzzification coefficients as opposed to only one coefficient in the original FCM algorithm. The new algorithm is then evaluated with experiments on several common datasets and the results show that the proposed algorithm is more efficient compared to the original FCM as well as other clustering methods.

Detection of Mobile Phone Fraud Using Possibilistic Fuzzy C-Means Clustering and Hidden Markov Model

2016 ◽  
Vol 7 (2) ◽  
pp. 23-44 ◽  
Author(s):  
Sharmila Subudhi ◽  
Suvasini Panigrahi ◽  
Tanmay Kumar Behera
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Mobile Phone ◽  
Hidden Markov ◽  
Clustering Methods ◽  
Fuzzy C Means ◽  
Novel Approach ◽  
Incoming Call ◽  
Fuzzy C Means Clustering ◽  
Reality Mining

This paper presents a novel approach for fraud detection in mobile phone networks by using a combination of Possibilistic Fuzzy C-Means clustering and Hidden Markov Model (HMM). The clustering technique is first applied on two calling features extracted from the past call records of a subscriber generating a behavioral profile for the user. The HMM parameters are computed from the profile, which are used to generate some profile sequences for training. The trained HMM model is then applied for detecting fraudulent activities on incoming call sequences. A calling instance is detected as forged when the new sequence is not accepted by the trained model with sufficiently high probability. The efficacy of the proposed system is demonstrated by extensive experiments carried out with Reality Mining dataset. Furthermore, the comparative analysis performed with other clustering methods and another approach recently proposed in the literature justifies the effectiveness of the proposed algorithm.

scholarly journals Long-term excess risk of breast cancer after a single breast density measurement

2019 ◽  
Vol 117 ◽  
pp. 41-47 ◽  
Author(s):  
Matejka Rebolj ◽  
Oleg Blyuss ◽  
Kee Seng Chia ◽  
Stephen W. Duffy
Keyword(s):  
Breast Cancer ◽  
Breast Density ◽  
Density Measurement ◽  
Excess Risk ◽  
Breast Density Measurement
Sign in / Sign up

Export Citation Format

Share Document