scholarly journals Adhesion Pulmonary Nodules Detection Based on Dot-Filter and Extracting Centerline Algorithm

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Liwei Liu ◽  
Xin Wang ◽  
Yang Li ◽  
Liping Wang ◽  
Jianghui Dong
Keyword(s):  
Pulmonary Nodule ◽  
Detection Method ◽  
Hessian Matrix ◽  
Pulmonary Nodules ◽  
Ct Images ◽  
Two Dimensional ◽  
Circular Area ◽  
Nodule Detection ◽  
Pulmonary Nodule Detection ◽  
Pulmonary Ct

A suspected pulmonary nodule detection method was proposed based on dot-filter and extracting centerline algorithm. In this paper, we focus on the distinguishing adhesion pulmonary nodules attached to vessels in two-dimensional (2D) lung computed tomography (CT) images. Firstly, the dot-filter based on Hessian matrix was constructed to enhance the circular area of the pulmonary CT images, which enhanced the circular suspected pulmonary nodule and suppresses the line-like areas. Secondly, to detect the nondistinguishable attached pulmonary nodules by the dot-filter, an algorithm based on extracting centerline was developed to enhance the circle area formed by the end or head of the vessels including the intersection of the lines. 20 sets of CT images were used in the experiments. In addition, 20 true/false nodules extracted were used to test the function of classifier. The experimental results show that the method based on dot-filter and extracting centerline algorithm can detect the attached pulmonary nodules accurately, which is a basis for further studies on the pulmonary nodule detection and diagnose.

Pulmonary Nodule Detection using Chest Ct Images

2003 ◽  
Vol 44 (3) ◽  
pp. 252-257 ◽  
Author(s):  
D.-Y. Kim ◽  
J.-H. Kim ◽  
S.-M. Noh ◽  
J.-W. Park
Keyword(s):  
Pulmonary Nodule ◽  
Ct Images ◽  
Chest Ct ◽  
Nodule Detection ◽  
Pulmonary Nodule Detection

scholarly journals Light Deep Model for Pulmonary Nodule Detection from CT Scan Images for Mobile Devices

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Mehedi Masud ◽  
Ghulam Muhammad ◽  
M. Shamim Hossain ◽  
Hesham Alhumyani ◽  
Sultan S. Alshamrani ◽  
...  
Keyword(s):  
Pulmonary Nodule ◽  
Data Analytics ◽  
Medical Image ◽  
Medical Image Analysis ◽  
Big Data Analytics ◽  
Pulmonary Nodules ◽  
Cognitive Computing ◽  
Deep Model ◽  
Nodule Detection ◽  
Pulmonary Nodule Detection

The emergence of cognitive computing and big data analytics revolutionize the healthcare domain, more specifically in detecting cancer. Lung cancer is one of the major reasons for death worldwide. The pulmonary nodules in the lung can be cancerous after development. Early detection of the pulmonary nodules can lead to early treatment and a significant reduction of death. In this paper, we proposed an end-to-end convolutional neural network- (CNN-) based automatic pulmonary nodule detection and classification system. The proposed CNN architecture has only four convolutional layers and is, therefore, light in nature. Each convolutional layer consists of two consecutive convolutional blocks, a connector convolutional block, nonlinear activation functions after each block, and a pooling block. The experiments are carried out using the Lung Image Database Consortium (LIDC) database. From the LIDC database, 1279 sample images are selected of which 569 are noncancerous, 278 are benign, and the rest are malignant. The proposed system achieved 97.9% accuracy. Compared to other famous CNN architecture, the proposed architecture has much lesser flops and parameters and is thereby suitable for real-time medical image analysis.

Pulmonary Nodule Detection in CT Images: False Positive Reduction Using Multi-View Convolutional Networks

2016 ◽  
Vol 35 (5) ◽  
pp. 1160-1169 ◽  
Author(s):  
Arnaud Arindra Adiyoso Setio ◽  
Francesco Ciompi ◽  
Geert Litjens ◽  
Paul Gerke ◽  
Colin Jacobs ◽  
...  
Keyword(s):  
Pulmonary Nodule ◽  
False Positive ◽  
Ct Images ◽  
Convolutional Networks ◽  
False Positive Reduction ◽  
Nodule Detection ◽  
Pulmonary Nodule Detection

A Quantitative Method for Differentiating Malignant and Benign Pulmonary Nodules

2014 ◽  
Vol 513-517 ◽  
pp. 3830-3834
Author(s):  
Yu Zhao ◽  
Sheng Dong Nie ◽  
Jie Wu ◽  
Yuan Jun Wang
Keyword(s):  
Pulmonary Nodule ◽  
Quantitative Method ◽  
Lung Nodule ◽  
Pulmonary Nodules ◽  
Lung Nodule Detection ◽  
Objective Standard ◽  
Nodule Detection ◽  
Pulmonary Nodule Detection ◽  
Definition Of ◽  
Important Basis

It is common sense that CAD has great significance in the lung nodule detection. But it is still controversial whether the CAD can also automatically differentiates between malignant and benign pulmonary nodules. The primary cause of this controversy is due to the subjective definition of 9 characteristics of nodules which are important basis of nodule identification. In other word, these characteristics are too dependent on the doctor scoring, and no objective standard of them has built which make these characteristics can be obtained by calculation.The main aim of this paper is to establish a quantitative method of the characteristics and refine these nine characteristics. This new method is used to find the objective replacement (a series features which can be measured through algorithms) of these subjective characteristics of the pulmonary nodule detection with Bayesian analysis.The experiment of our method proves that it is feasible to substitute the features of Pulmonary Nodule obtained by calculating for the characteristics of the nodule which only used to be gotten by the subjective judgment of doctors.

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