Automatic Extraction of Lung Regions Using Curve Analysis for the Detection of Lung Cancer in CT Scans

Sasidhar, B; Rao, Naseem; Babu, D. R. Ramesh; Shankar, M. Ravi

doi:10.1007/978-981-10-0287-8_32

Cited by 2 publications

(2 citation statements)

References 16 publications

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“…Accurate lung segmentation allows for the detection and quantification of abnormalities within the lungs. Segmentation in this paper is based on threshold and morphological operations [11]. The filtered CT image shown in Fig.3.a is converted to binary image using adaptive threshold and the result is shown in Fig.3.b.…”

Section: Segmentation Of Lung Region and Roimentioning

confidence: 99%

Lung Cancer Classification using Curvelet Transform and Neural Network with Radial Basis Function

Obayya¹,

Ghandour²

2015

IJCA

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In this paper, computed tomographic (CT) chest images were investigated to develop an automated system to discriminate lung cancer. These were done by analyzing Data recorded for patients with benign cancer, and also patients with malignant lung cancer were taken in account. The techniques for utilized feature extraction included features derived from texture analysis based on Gray Level Co-occurrence Matrix (GLCM) of the input image, as well as features derived from curvelet transform-based features. An artificial neural network (ANN) with radial basis function classifier was utilized to classify the type of cancer whether benign or malignant. The results have shown that using curvelet domain features gives the highest rate to recognize lung cancer. Classification correct rate is up to 96%. KeywordsCurvelet transform, lung cancer, image processing, ANN with radial basis function. Negative(Benign)1

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Section: Segmentation Of Lung Region and Roimentioning

confidence: 99%

Lung Cancer Classification using Curvelet Transform and Neural Network with Radial Basis Function

Obayya¹,

Ghandour²

2015

IJCA

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“…They have examined the diagnostic performances of FIS system through Artificial Neural Networks (ANNs). In Sasidhar et al (2013) have concerned two steps a. Automated Segmentation of lung regions b.…”

Section: Introductionmentioning

confidence: 99%

Segmentation of Lungs via Hybridization of CA and Level Set Algorithm

Star¹,

Subburaj²

2014

RJASET

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In this study, we present a fast and robust practical tool for segmentation of solid tumors with minimal user interaction. The lung image is segmented manually by the experts may includes risk and time consuming process. Hence, in this study, the lung image is segmented in order to identify the tumor sector. Initially, the input lung image is applied with the denoising process for removing noises with the aid of multi-wavelet transformation. After this process, the CA algorithm is applied for obtaining the background seeds and foreground seeds (tumor seeds) and then the level set algorithm is applied for acquiring the acute tumor tissues. As a result of the mentioned process, the tumor sector is segmented and the results are depicted. Studies on lung tumor datasets demonstrate 80-85% overlap performance of the proposed algorithm with less sensitivity to seed initialization, robustness with respect to heterogeneous tumor types and its efficiency in terms of computation time.

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Automatic Extraction of Lung Regions Using Curve Analysis for the Detection of Lung Cancer in CT Scans

Cited by 2 publications

References 16 publications

Lung Cancer Classification using Curvelet Transform and Neural Network with Radial Basis Function

Lung Cancer Classification using Curvelet Transform and Neural Network with Radial Basis Function

Segmentation of Lungs via Hybridization of CA and Level Set Algorithm

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