Pulmonary Tumor Volume Detection from Positron Emission Tomography Images

Kanakatte, Aparna; Mani, N.; Srinivasan, Bala; Giibbi, J.

doi:10.1109/bmei.2008.354

Cited by 16 publications

(8 citation statements)

References 16 publications

(17 reference statements)

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“…As low dose CT has been proven to minimize mortality, the issues of CT screening are recognized as manageable, with the possible exception of cost (Gould et al, 2013). With PET images, greater accuracy of 97% is accomplished through the use of the SVM classifier (Kanakatte et al, 2008). Numerous advantages of SVMs include efficiency with high-dimensional spaces, maintained efficiency even when the number of dimensions exceeds the sample number, and memory efficiency owing to the use of a subset of training points in the decision function (called support vector).…”

Section: Resultsmentioning

confidence: 99%

Artificial Intelligence Techniques for Cancer Detection and Classification: Review Study

Al-Shamasneh¹,

Obaidellah²

2017

ESJ

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Cancer is the general name for a group of more than 100 diseases. Although cancer includes different types of diseases, they all start because abnormal cells grow out of control. Without treatment, cancer can cause serious health problems and even loss of life. Early detection of cancer may reduce mortality and morbidity. This paper presents a review of the detection methods for lung, breast, and brain cancers. These methods used for diagnosis include artificial intelligence techniques, such as support vector machine neural network, artificial neural network, fuzzy logic, and adaptive neuro-fuzzy inference system, with medical imaging like X-ray, ultrasound, magnetic resonance imaging, and computed tomography scan images. Imaging techniques are the most important approach for precise diagnosis of human cancer. We investigated all these techniques to identify a method that can provide superior accuracy and determine the best medical images for use in each type of cancer.

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Section: Resultsmentioning

confidence: 99%

Artificial Intelligence Techniques for Cancer Detection and Classification: Review Study

Al-Shamasneh¹,

Obaidellah²

2017

ESJ

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“…Lung carcinoma is one of the most lethal of cancers worldwide [89]. Positron emission tomography (PET) data has greater sensitivity and specificity in the staging of lung cancer than computed tomography (CT) or magnetic resonance imaging (MRI).…”

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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“…Aparna kanatte et al [1] describes Lung carcinoma is one of the most lethal of cancers worldwide. Positron emission tomography (PET) data has greater sensitivity and specificity in the staging of lung cancer than computed tomography (CT) or magnetic resonance imaging (MRI).…”

Section: Related Workmentioning

confidence: 99%

“…For the analysis of the proposed method CT image is sufficient also the visibility of soft tissue is better. There are several types of lung cancer, and these are divided into two main groups: Small cell lung cancer and non-small cell lung cancer which has three subtypes: Carcinoma, Adeno carcinoma and Squamous cell carcinomas [1].…”

Section: Introductionmentioning

confidence: 99%

Detection of Lung Cancer Using Digital Image Processing Techniques: A Comparative Study

Chander¹,

Rao²,

Rajinikanth³

2017

IJMI

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This paper focuses on early stage lung cancer detection. Lung cancer is prominent cancer as it states large number of deaths of more than a million every year. It creates need of detecting the lung nodule at early stage in Computer Tomography medical images. So to detect the occurrence of cancer nodule at early stage, the requirement of methods and techniques is increasing. There are different methods and techniques existing but none of them provide a better accuracy of detection. One of the techniques is content based image retrieval Computer Aided Diagnosis System (CAD) for early detection of lung nodules from the Chest Computer Tomography (CT) images. This optimization algorithm allows physicians to identify the nodules present in the CT lung images in the early stage hence the lung cancer. The MATLAB image processing toolbox based implementation is done on the CT lung images and the classifications of these images are carried out. The performance measures like the classification rate and the false positive rates are analyzed.

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Pulmonary Tumor Volume Detection from Positron Emission Tomography Images

Cited by 16 publications

References 16 publications

Artificial Intelligence Techniques for Cancer Detection and Classification: Review Study

Artificial Intelligence Techniques for Cancer Detection and Classification: Review Study

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

Detection of Lung Cancer Using Digital Image Processing Techniques: A Comparative Study

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