scholarly journals Fuzzy Clustering Applied to ROI Detection in Helical Thoracic CT Scans with a New Proposal and Variants

2016 ◽  
Vol 2016 ◽  
pp. 1-15
Author(s):  
Alfonso Castro ◽  
Alberto Rey ◽  
Carmen Boveda ◽  
Bernardino Arcay ◽  
Pedro Sanjurjo
Keyword(s):  
Fuzzy Clustering ◽  
Social Impact ◽  
Traditional Approach ◽  
Medical Image Analysis ◽  
Clustering Algorithms ◽  
Pulmonary Nodules ◽  
Great Difficulty ◽  
Ct Scans ◽  
Cad System ◽  
Thoracic Ct

The detection of pulmonary nodules is one of the most studied problems in the field of medical image analysis due to the great difficulty in the early detection of such nodules and their social impact. The traditional approach involves the development of a multistage CAD system capable of informing the radiologist of the presence or absence of nodules. One stage in such systems is the detection of ROI (regions of interest) that may be nodules in order to reduce the space of the problem. This paper evaluates fuzzy clustering algorithms that employ different classification strategies to achieve this goal. After characterising these algorithms, the authors propose a new algorithm and different variations to improve the results obtained initially. Finally it is shown as the most recent developments in fuzzy clustering are able to detect regions that may be nodules in CT studies. The algorithms were evaluated using helical thoracic CT scans obtained from the database of the LIDC (Lung Image Database Consortium).

Computer-assisted detection (CAD) of pulmonary nodules on thoracic CT scans using image processing and classification techniques

2004 ◽  
Author(s):  
Jamshid Dehmeshki ◽  
Manlio Valdivieso-Casique ◽  
Musib Siddique ◽  
Mandana E. Dehkordi ◽  
John Costello ◽  
...  
Keyword(s):  
Image Processing ◽  
Pulmonary Nodules ◽  
Ct Scans ◽  
Computer Assisted ◽  
Classification Techniques ◽  
Thoracic Ct ◽  
Computer Assisted Detection

Recurrent attention network for false positive reduction in the detection of pulmonary nodules in thoracic CT scans

2020 ◽  
Vol 47 (5) ◽  
pp. 2150-2160 ◽  
Author(s):  
M. Mehdi Farhangi ◽  
Nicholas Petrick ◽  
Berkman Sahiner ◽  
Hichem Frigui ◽  
Amir A. Amini ◽  
...  
Keyword(s):  
False Positive ◽  
Pulmonary Nodules ◽  
Ct Scans ◽  
Attention Network ◽  
Thoracic Ct ◽  
False Positive Reduction

scholarly journals Segmentation of Pulmonary Nodules in Thoracic CT Scans: A Region Growing Approach

2008 ◽  
Vol 27 (4) ◽  
pp. 467-480 ◽  
Author(s):  
J. Dehmeshki ◽  
H. Amin ◽  
M. Valdivieso ◽  
Xujiong Ye
Keyword(s):  
Pulmonary Nodules ◽  
Region Growing ◽  
Ct Scans ◽  
Thoracic Ct

scholarly journals Recognition of Pulmonary Nodules in Thoracic CT Scans Using 3-D Deformable Object Models of Different Classes

Algorithms ◽  
2010 ◽  
Vol 3 (2) ◽  
pp. 125-144 ◽  
Author(s):  
Hotaka Takizawa ◽  
Shinji Yamamoto ◽  
Tsuyoshi Shiina
Keyword(s):  
Pulmonary Nodules ◽  
Ct Scans ◽  
Deformable Object ◽  
Thoracic Ct ◽  
Object Models

AN EXTENDED FUZZY CLUSTERING ALGORITHM AND ITS APPLICATION

1995 ◽  
Vol 05 (02) ◽  
pp. 239-259
Author(s):  
SU HWAN KIM ◽  
SEON WOOK KIM ◽  
TAE WON RHEE
Keyword(s):  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Main Memory ◽  
Color Image Segmentation ◽  
Occurrence Rate ◽  
Secondary Memory ◽  
Worst Case ◽  
Memory Space ◽  
Fuzzy Clustering Algorithm

For data analyses, it is very important to combine data with similar attribute values into a categorically homogeneous subset, called a cluster, and this technique is called clustering. Generally crisp clustering algorithms are weak in noise, because each datum should be assigned to exactly one cluster. In order to solve the problem, a fuzzy c-means, a fuzzy maximum likelihood estimation, and an optimal fuzzy clustering algorithms in the fuzzy set theory have been proposed. They, however, require a lot of processing time because of exhaustive iteration with an amount of data and their memberships. Especially large memory space results in the degradation of performance in real-time processing applications, because it takes too much time to swap between the main memory and the secondary memory. To overcome these limitations, an extended fuzzy clustering algorithm based on an unsupervised optimal fuzzy clustering algorithm is proposed in this paper. This algorithm assigns a weight factor to each distinct datum considering its occurrence rate. Also, the proposed extended fuzzy clustering algorithm considers the degree of importances of each attribute, which determines the characteristics of the data. The worst case is that the whole data has an uniformly normal distribution, which means the importance of all attributes are the same. The proposed extended fuzzy clustering algorithm has better performance than the unsupervised optimal fuzzy clustering algorithm in terms of memory space and execution time in most cases. For simulation the proposed algorithm is applied to color image segmentation. Also automatic target detection and multipeak detection are considered as applications. These schemes can be applied to any other fuzzy clustering algorithms.

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