Automated detection of lung nodules in CT images using shape-based genetic algorithm

Dehmeshki, Jamshid; Ye, Xujiong; Lin, Xinyu; Valdivieso, M.; Amin, Hamdan

doi:10.1016/j.compmedimag.2007.03.002

Cited by 110 publications

(64 citation statements)

References 16 publications

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“…Lee et al [17] presented a novel template-matching technique with the help of genetic algorithm template-matching technique for nodule detection. Dehmeshki et al [18] enhanced this method www.ijacsa.thesai.org by adding a shape-based methodology for nodule detection from spherical elements. Tan et al [19] utilized the three classifiers such as artificial neural network and genetic algorithm for lung nodule detection.…”

Section: Introductionmentioning

confidence: 99%

An Improved Pulmonary Nodule Detection Scheme based on Multi-Layered Filtering and 3d Distance Metrics

Jahangir¹,

Imran²,

Abbas³

et al. 2016

ijacsa

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Abstract-This paper proposed a computer-aided detection (CAD) system to automatically detect pulmonary nodules from thoracic computed tomography (CT) images. Automatically detect pulmonary nodules is a difficult job because of the large deviation in size, shape, location and density of nodules. The proposed CAD scheme applies multiple 3D disk-shaped laplacian filters to enhance the shape of spherical regions. Optimal multiple thresholding and 3D distance mapping is used to extract regions of interest and separate nodules. Finally, rule-based pruning removes easily dismissible false positive structures. The proposed system provides an overall nodule detection rate of 80% with an average of 12.2 false positives per scan. The experimental results reveals that the proposed CAD can attain a comparatively high performance.

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Section: Introductionmentioning

confidence: 99%

An Improved Pulmonary Nodule Detection Scheme based on Multi-Layered Filtering and 3d Distance Metrics

Jahangir¹,

Imran²,

Abbas³

et al. 2016

ijacsa

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“…Finally, nodules were detected through various classifiers using the extracted feature vectors with a small amount of false positives. The rule-based filtering method [7] and linear discriminant analysis classifier [5,8] have been generally used. In addition, machine learning-based classification methods have been also used for false positive reduction.…”

Section: Introductionmentioning

confidence: 99%

“…Multiple gray-level thresholding [5,6,15] is one of the most popular methods. Moreover, template-matching-based methods [7], shape-based methods [9,16], filtering-based methods [8], and morphological approaches with convexity models [17] have been used to detect nodule candidates. In this step, there were many false positives which were reduced through false positive reduction.…”

Section: Introductionmentioning

confidence: 99%

“…There are a variety of methods for extracting lung volume from a pulmonary CT scan. In order to segment lung, approaches such as global thresholding [5,6], optimal thresholding [7,8], three-dimensional (3-D)-adaptive fuzzy thresholding [9], rule-based region growing [10], connected component labeling [11], graph-cut algorithm [12], and hybrid segmentation [13] have generally been used. After initial segmentation, lung volume has been extracted using several methods, and the extracted lung volume needed to be refined to include juxta-pleural nodules.…”

Section: Introductionmentioning

confidence: 99%

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Automated Pulmonary Nodule Detection System in Computed Tomography Images: A Hierarchical Block Classification Approach

Choi

2013

Entropy

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A computer-aided detection (CAD) system is helpful for radiologists to detect pulmonary nodules at an early stage. In this paper, we propose a novel pulmonary nodule detection method based on hierarchical block classification. The proposed CAD system consists of three steps. In the first step, input computed tomography images are split into three-dimensional block images, and we apply entropy analysis on the block images to select informative blocks. In the second step, the selected block images are segmented and adjusted for detecting nodule candidates. In the last step, we classify the nodule candidate images into nodules and non-nodules. We extract feature vectors of the objects in the selected blocks. Lastly, the support vector machine is applied to classify the extracted feature vectors. Performance of the proposed system is evaluated on the Lung Image Database Consortium database. The proposed method has reduced the false positives in the nodule candidates significantly. It achieved 95.28% sensitivity with only 2.27 false positives per scan.

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“…e Huang et al (WEIMIN HUANG et al, 2014;ZHU et al, 2015) No trabalho de Dehmeshki et al (2007), o uso de uma meta-heurística associada a um modelo morfológico demonstrou a capacidade de algoritmos de otimização na busca de lesões pulmonares (DEHMESHKI et al, 2007 …”

Section: Introductionunclassified

Modelo de aprendizado de máquina com fronteira de decisão fechada para identificação de carcinoma hepatocelular inicial

Thomaz¹

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Automated detection of lung nodules in CT images using shape-based genetic algorithm

Cited by 110 publications

References 16 publications

An Improved Pulmonary Nodule Detection Scheme based on Multi-Layered Filtering and 3d Distance Metrics

An Improved Pulmonary Nodule Detection Scheme based on Multi-Layered Filtering and 3d Distance Metrics

Automated Pulmonary Nodule Detection System in Computed Tomography Images: A Hierarchical Block Classification Approach

Modelo de aprendizado de máquina com fronteira de decisão fechada para identificação de carcinoma hepatocelular inicial

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