Detection of lung cancer nodules using automatic region growing method

Parveen, Shahida; Kavitha, C.

doi:10.1109/icccnt.2013.6726669

Cited by 21 publications

(11 citation statements)

References 13 publications

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“…Automatic lung nodule detection scheme in [90] is presented in Multi-Slice Computed Tomography (MSCT) scans using SVM. An automatic CAD system is developed for early detection of lung nodule by analyzing lung CT images which achieves 80% result accuracy [91].…”

Section: Discussionmentioning

confidence: 99%

Analyzing the Dynamics of Lung Cancer Imaging Data Using Refined Fuzzy Entropy Methods by Extracting Different Features

et al. 2019

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Lung cancer is the major cause of cancer-related deaths worldwide with poor survival due to the poor diagnostic system at the advanced cancer stage. In the past, researchers developed computeraided diagnosis (CAD) systems, which were greatly used by the radiologist for identifying the abnormalities and applied few features extracting methods. The physiology and behavior of various physiological systems can be best investigated using nonlinear dynamical measures for capturing the intrinsic dynamics, which is influenced due to multiple pathologies by the degradation of structural and functional components. As cancer images contain hidden information, which can be best analyzed using these dynamical measures. In this paper, we proposed multiscale sample entropy (MSE) with a mean and KD-tree algorithmic approach, multiscale permutation entropy (MPE), multiscale fuzzy entropy (MFE), and refined composite multiscale fuzzy entropy (RCMFE) with mean, variance, and standard deviation. The statistically significant results were computed to distinguish non-small-cell lung cancer (NSCLC) from SCLC by extracting morphological, texture, and elliptic Fourier descriptors (EFDs). The highest significant results obtained based on texture features using MFE with standard deviation give the P-value of 1.95E-50, morphological features using RCMFE with mean provide the P-value of 3.01E-14, and EFDs features using MFE with variance give the P-value of 1.04E-13. The results reveal that the improved complexity measures based on refined fuzzy entropy outperformed in analyzing the dynamics of lung cancer and will provide a new insight into extract meaningful hidden information present in the Lung cancer images, which will be very helpful to further distinguish NSCLC and SCLC for early diagnosis and prognosis. II. MATERIAL AND METHODSA. DATA SET

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Section: Discussionmentioning

confidence: 99%

Analyzing the Dynamics of Lung Cancer Imaging Data Using Refined Fuzzy Entropy Methods by Extracting Different Features

et al. 2019

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“…The surface is updated with force derived from the image. A point at intersection at time satisfies (10).…”

Section: Active Contour (Snake Model and Level Set Model)mentioning

confidence: 99%

A Review of Image Segmentation Methods for Lung Nodule Detection Based on Computed Tomography Images

Zheng

Lei

2018

MATEC Web Conf.

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The detection and segmentation of lung nodules based on computer tomography images (CT) is a basic and significant step to achieve the robotic needle biopsy. In this paper, we reviewed some typical segmentation algorithms, including thresholding, active contour, differential operator, region growing and watershed. To analyse their performance on lung nodule detection, we applied them to four CT images of different kinds of lung nodules. The results show that thresholding, active contour and differential operator do well in the segmentation of solitary nodules, while region growing has an advantage over the others on segmenting nodules adhere to vessels. For segmentation of semi-transparent nodules, differential operator is an especially suitable choice. Watershed can segment nodules adhere to vessels and semi-transparent nodules well, but it has low sensitivity in solitary nodules.

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“…After performing the segmentation [15], the features have to be extracted for detecting the cancer in the lung region correctly. This step concerns with two feature extraction such as Gray level co-occurrence Matrix (GLCM).…”

Section: Methodsmentioning

confidence: 99%

“…By analyzing the materials [13] we proposed automatic region growing method for segmentation [15]. For feature extraction GLCM is used and SVM kernels for classification to diagnose the occurrence of lung cancer.…”

Section: Previous Researchmentioning

confidence: 99%

Classification of Lung Cancer Nodules using SVM Kernels

Parveen¹,

Kavitha²

2014

IJCA

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Support Vector Machines (SVM) is a machine learning method used for classifying the system. It analyses and identifies the categories using the trained data. It is widely used in medical field for diagnosing the disease. The proposed method consists of four phases. They are lung extraction, segmentation of lung region, feature extraction and finally classification of normal, benign and malignancy in the lung. Threat pixel identification with region growing method is used for segmentation of focal areas in the lung. For feature extraction gray level co-occurrence Matrix (GLCM) is been used. Extracted features are classified using different kernels of Support Vector Machine (SVM). The experimentation is performed with the help of real time computer tomography images.

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Detection of lung cancer nodules using automatic region growing method

Cited by 21 publications

References 13 publications

Analyzing the Dynamics of Lung Cancer Imaging Data Using Refined Fuzzy Entropy Methods by Extracting Different Features

Analyzing the Dynamics of Lung Cancer Imaging Data Using Refined Fuzzy Entropy Methods by Extracting Different Features

A Review of Image Segmentation Methods for Lung Nodule Detection Based on Computed Tomography Images

Classification of Lung Cancer Nodules using SVM Kernels

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