scholarly journals Lung Nodule Detection in CT Images Using Statistical and Shape-Based Features

2020 ◽  
Vol 6 (2) ◽  
pp. 6
Author(s):  
Noor Khehrah ◽  
Muhammad Shahid Farid ◽  
Saira Bilal ◽  
Muhammad Hassan Khan
Keyword(s):  
Lung Tumor ◽  
Lung Nodule ◽  
Ct Images ◽  
Occurrence Rate ◽  
Support Vector ◽  
High Death Rate ◽  
Internal Structures ◽  
Nodule Detection ◽  
High Death ◽  
Sensitivity Rate

The lung tumor is among the most detrimental kinds of malignancy. It has a high occurrence rate and a high death rate, as it is frequently diagnosed at the later stages. Computed Tomography (CT) scans are broadly used to distinguish the disease; computer aided systems are being created to analyze the ailment at prior stages productively. In this paper, we present a fully automatic framework for nodule detection from CT images of lungs. A histogram of the grayscale CT image is computed to automatically isolate the lung locale from the foundation. The results are refined using morphological operators. The internal structures are then extracted from the parenchyma. A threshold-based technique is proposed to separate the candidate nodules from other structures, e.g., bronchioles and blood vessels. Different statistical and shape-based features are extracted for these nodule candidates to form nodule feature vectors which are classified using support vector machines. The proposed method is evaluated on a large lungs CT dataset collected from the Lung Image Database Consortium (LIDC). The proposed method achieved excellent results compared to similar existing methods; it achieves a sensitivity rate of 93.75%, which demonstrates its effectiveness.

scholarly journals Lung Nodule Detection of CT and Image-Based GLCM and RLM CT Scan Using the Support Vector Machine (SVM) Method

2021 ◽  
Vol 5 (2) ◽  
Author(s):  
Zaimah Permatasari ◽  
Mauridhi Hery Purnomo ◽  
I Ketut Eddy Purnama
Keyword(s):  
Lung Cancer ◽  
Support Vector Machine ◽  
Lung Nodule ◽  
Region Of Interest ◽  
Medical Personnel ◽  
Ct Images ◽  
Support Vector ◽  
Nodule Detection ◽  
The Status ◽  
Keywords Cancer

Lung cancer is the most common cause of cancer death globally. Early detection of lung cancer will greatly beneficial to save the patient. This study focused on the detection of lung cancer using classification with the Support Vector Machine (SVM) method based on the features of Gray Level Co-occurrence Matrices (GLCM) and Run Length Matrix (RLM). The lung data used were obtained from the Cancer imaging archive Database, consisting of 500 CT images. CT images were grouped into 2 clusters, including normal and lung cancer. The research steps include: image processing, region of interest segmentation, and feature extraction. The results indicate that the system can detect the CT-image of SVM classification where the default parameter only provides an accuracy of 85.63%. It is expected that the results will be useful to help medical personnel and researchers to detect the status of lung cancer. These results provide information that detection of lung nodules based on GLCM and RLM features that can be detected is better. Furthermore, selecting parameters C and γ on SVM. Keywords: cancer, nodule, support vector machine (SVM).

scholarly journals AUTOMATIC LUNG NODULE DETECTION BASED ON STATISTICAL REGION MERGING AND SUPPORT VECTOR MACHINES

2017 ◽  
Vol 36 (2) ◽  
pp. 65 ◽  
Author(s):  
Elaheh Aghabalaei Khordehchi ◽  
Ahmad Ayatollahi ◽  
Mohammad Reza Daliri
Keyword(s):  
Lung Nodule ◽  
Three Dimensional ◽  
Pulmonary Nodules ◽  
Ct Images ◽  
Support Vector ◽  
Svm Classifier ◽  
Region Merging ◽  
Three Dimensional Objects ◽  
Ct Data ◽  
Statistical Region Merging

Lung cancer is one of the most common diseases in the world that can be treated if the lung nodules are detected in their early stages of growth. This study develops a new framework for computer-aided detection of pulmonary nodules thorough a fully-automatic analysis of Computed Tomography (CT) images. In the present work, the multi-layer CT data is fed into a pre-processing step that exploits an adaptive diffusion-based smoothing algorithm in which the parameters are automatically tuned using an adaptation technique. After multiple levels of morphological filtering, the Regions of Interest (ROIs) are extracted from the smoothed images. The Statistical Region Merging (SRM) algorithm is applied to the ROIs in order to segment each layer of the CT data. Extracted segments in consecutive layers are then analyzed in such a way that if they intersect at more than a predefined number of pixels, they are labeled with a similar index. The boundaries of the segments in adjacent layers which have the same indices are then connected together to form three-dimensional objects as the nodule candidates. After extracting four spectral, one morphological, and one textural feature from all candidates, they are finally classified into nodules and non-nodules using the Support Vector Machine (SVM) classifier. The proposed framework has been applied to two sets of lung CT images and its performance has been compared to that of nine other competing state-of-the-art methods. The considerable efficiency of the proposed approach has been proved quantitatively and validated by clinical experts as well.

Automatic Lung Nodule Detection in CT Images Using Convolutional Neural Networks

2019 ◽  
Vol E102.A (10) ◽  
pp. 1364-1373
Author(s):  
Furqan SHAUKAT ◽  
Kamran JAVED ◽  
Gulistan RAJA ◽  
Junaid MIR ◽  
Muhammad Laiq Ur Rahman SHAHID
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Lung Nodule ◽  
Ct Images ◽  
Lung Nodule Detection ◽  
Nodule Detection

A novel computer-aided lung nodule detection system for CT images

2011 ◽  
Vol 38 (10) ◽  
pp. 5630-5645 ◽  
Author(s):  
Maxine Tan ◽  
Rudi Deklerck ◽  
Bart Jansen ◽  
Michel Bister ◽  
Jan Cornelis
Keyword(s):  
Detection System ◽  
Lung Nodule ◽  
Ct Images ◽  
Lung Nodule Detection ◽  
Nodule Detection ◽  
Computer Aided
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