Paper Info
Title
Applying Bayesian Network Approach to Determine the Association Between Morphological Features Extracted from Prostate Cancer Images.
Abstract
Cancer is a major public health problem across the globe due to which millions of deaths occur every year. In the United States, prostate cancer is the second leading cause of cancer- related deaths in men. The major causes of prostate cancer include increasing age, family history, diet, sexual behavior, and geographic location. Early detection of prostate cancer can effectively reduce the mortality rate. In the past, researchers have adopted various multimodal feature extracting strategies to extract diverse and comprehensive quantitative imaging features and employed machine learning methods to detect prostate cancer. However, existing techniques lack detailed analysis of the magnitude of relationship among different individual discriminatory features, which is very important to understand the dynamics of the disease. In this study, we extracted diverse morphological features to summarize the imaging profile of patients of prostate cancer imaging database and employed Bayesian network analysis approach to quantify the association between different features and the strength of the association. The features and the association between the features were, respectively, modeled as the nodes and the edges of the network. The strength of association between the nodes was computed using Pearson's correlation, mutual Information and Kullback- Liebler methods. The strongest associations were found between multiple features: (Area -> Equidiameter), (Area -> Circulatory 2), (Circulatory 1 -> (Elongatedness), (Circulatory 1 -> Entropy), (Circulatory 1 -> Max. Radius), and (MM. Radius -> Eccentricity). Moreover, interaction impact among nodes and node force was also computed. This analysis will help in finding the features that are more dominant to establish the relationship and can further increase the detection performance.
Year
DOI
Venue
2019
10.1109/ACCESS.2018.2886644
IEEE ACCESS
Keywords
Field
DocType
Bayesian network analysis,coherence,prostate cancer,morphological features,Pearson's correlation,mutual information,Kullback Liebler
Early detection,Computer science,Bayesian network,Correlation,Mutual information,Prostate cancer,Quantitative imaging,Statistics,Cancer,Mortality rate,Distributed computing
Journal
Volume
ISSN
Citations 
7
2169-3536
0
PageRank 
References 
Authors
0.34
0
8
Name
Order
Citations
PageRank
Lal Hussain112.08
Amjad Ali210432.10
Saima Rathore39511.74
Sharjil Saeed421.77
idris5494.06
Muhammad Usama Usman600.34
Muhammad Aksam Iftikhar7817.42
Doug Young Suh87719.96