Autonomous detection of pulmonary nodules on CT images with a neural network-based fuzzy system

Lin, Daw-Tung; Yan, Chung-Ren; Chen, Wentai

doi:10.1016/j.compmedimag.2005.04.001

Cited by 66 publications

(35 citation statements)

References 27 publications

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“…Combining the region growing with morphological closing, Lin and Yan [62] and Lin et al [63] succeed to fill the large indentation caused by blood vessel that could not be extracted by thresholding.…”

Section: ) 2d-based Approachesmentioning

confidence: 99%

“…A two-level convolution neural network was proposed in Lin et al [86]. Lin and Yan [62] and Lin et al [63] combined fuzzy logic and neural networks for lung nodule detection and reported that the combination was superior to rule-base, convolution neural network, and genetic algorithm template matching approaches. Also, Antonelli et al [4] adopted a decision fusion technique to develop a computer-aided detection (CAD) system for automatic detection of pulmonary nodules in low-dose CT images.…”

Section: Nodule Extraction and Classificationmentioning

confidence: 99%

See 1 more Smart Citation

Lung Cancer Detection on CT Scan Images: A Review on the Analysis Techniques

Mahersia¹,

Zaroug²,

Gabralla³

2015

ijarai

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Abstract-Lung nodules are potential manifestations of lung cancer, and their early detection facilitates early treatment and improves patient's chances for survival. For this reason, CAD systems for lung cancer have been proposed in several studies. All these works involved mainly three steps to detect the pulmonary nodule: preprocessing, segmentation of the lung and classification of the nodule candidates. This paper overviews the current state-of-the-art regarding all the approaches and techniques that have been investigated in the literature. It also provides a comparison of the performance of the existing approaches.

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Section: ) 2d-based Approachesmentioning

confidence: 99%

Section: Nodule Extraction and Classificationmentioning

confidence: 99%

Lung Cancer Detection on CT Scan Images: A Review on the Analysis Techniques

Mahersia¹,

Zaroug²,

Gabralla³

2015

ijarai

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“…In their work, the decisions were based on crisp rules. Threshold based segmentation approach for segmenting lung region was proposed by Lin DT et al [7]. They have used a 5x5 median filter for removing the noise.…”

Section: Related Workmentioning

confidence: 99%

Intelligent Approach for Segmenting CT Lung Images Using Fuzzy Logic with Bitplane

Khan

Kannan²

2014

Journal of Electrical Engineering and Technology

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-In this article, we present a new grey scale image segmentation method based on Fuzzy logic and bitplane techniques which combines the bits of different bitplanes of a pixel inorder to increase the segmentation quality and to get a more reliable and accurate segmentation result. The proposed segmentation approach is conceptually different and explores a new strategy. Infact, our technique consists in combining many realizations of the image together inorder to increase the information quality and to get an optimal segmented image. For segmentation, we proceed in two steps. In the first step, we begin by identifying the bitplanes that represent the lungs clearly. For this purpose, the intensity value of a pixel is separated into bitplanes. In the second step, segmentation values are assigned for each bitplane based on membership table. The segmented values of foreground are combined and the segmentation values of background are combined. The algorithm is demonstrated through the medical computed tomography (CT) images. The segmentation accuracy of the proposed method is compared with two existing techniques. Satisfactory segmentation results have been obtained showing the effectiveness and superiority of the proposed method.

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“…Lin DT et al [10] proposed a novel threshold based segmentation approach for segmenting lung region present in the CT lung images. In their work, during preprocessing they have used a 5x5 median filter for removing the noise present in it.…”

Section: Related Workmentioning

confidence: 99%