Automatic Lung Nodule Detection Using Template Matching

Özekes, Serhat; Camurcu, Ali Yilmaz

doi:10.1007/11890393_26

Cited by 17 publications

(6 citation statements)

References 6 publications

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“…To reduce the complexity of the system, the ROIs were extracted using 8 directional ROI specification methods, which were introduced by Ozekes et al ( 19 , 20 ), to detect he mass candidates. Instead of scanning the whole CT slices with the template voxel by voxel, only the ROIs were considered in the scan.…”

Section: Methodsmentioning

confidence: 99%

Nodule Detection in a Lung Region that's Segmented with Using Genetic Cellular Neural Networks and 3D Template Matching with Fuzzy Rule Based Thresholding

2008

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ObjectiveThe purpose of this study was to develop a new method for automated lung nodule detection in serial section CT images with using the characteristics of the 3D appearance of the nodules that distinguish themselves from the vessels.Materials and MethodsLung nodules were detected in four steps. First, to reduce the number of region of interests (ROIs) and the computation time, the lung regions of the CTs were segmented using Genetic Cellular Neural Networks (G-CNN). Then, for each lung region, ROIs were specified with using the 8 directional search; +1 or -1 values were assigned to each voxel. The 3D ROI image was obtained by combining all the 2-Dimensional (2D) ROI images. A 3D template was created to find the nodule-like structures on the 3D ROI image. Convolution of the 3D ROI image with the proposed template strengthens the shapes that are similar to those of the template and it weakens the other ones. Finally, fuzzy rule based thresholding was applied and the ROI's were found. To test the system's efficiency, we used 16 cases with a total of 425 slices, which were taken from the Lung Image Database Consortium (LIDC) dataset.ResultsThe computer aided diagnosis (CAD) system achieved 100% sensitivity with 13.375 FPs per case when the nodule thickness was greater than or equal to 5.625 mm.ConclusionOur results indicate that the detection performance of our algorithm is satisfactory, and this may well improve the performance of computer-aided detection of lung nodules.

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Section: Methodsmentioning

confidence: 99%

Nodule Detection in a Lung Region that's Segmented with Using Genetic Cellular Neural Networks and 3D Template Matching with Fuzzy Rule Based Thresholding

2008

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“…Serhat Ozekes et al, [1] proposed a system for detecting lung nodules. A nodule template-based algorithm was employed to categorize the region of interest, instead of detecting the ROI's using density value of the pixels in the CT image.…”

Section: Related Workmentioning

confidence: 99%

Multiple Object Detection in Images using Template Matching

Geethapriya*,

Devaki,

Geethapriya

2019

IJITEE

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Template matching is an application in computer vision for finding similar objects between the images. Template matching is a key component in image analysis process for mapping similar patterns between images. The detection and matching of objects has gained widespread application in quality control, manufacturing and medical applications. The major challenges in template matching process are occlusion, background noise and non-rigid transformation particularly in medical images. The present work proposes a template matching method based on correlation analysis techniques to detect multiple objects occurrence in an image. The method has been experimented for various real-time applications. In medical images, the proposed method gave an accuracy of 86% in detecting the nodules in lung CT images

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“…Until now, many CAD systems have been proposed. For example, Ozekes and Camurcu proposed a method for pulmonary nodule detection method using template matching [ 7 ]. Schilham et al presented a CAD system which consists of image preprocessing, candidate nodule detection, feature extraction, and classification for nodule detection in chest radiographs [ 8 ].…”

Section: Related Workmentioning

confidence: 99%

A Computer-Aided Pipeline for Automatic Lung Cancer Classification on Computed Tomography Scans

Dandıl

2018

Journal of Healthcare Engineering

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Lung cancer is one of the most common cancer types. For the survival of the patient, early detection of lung cancer with the best treatment method is crucial. In this study, we propose a novel computer-aided pipeline on computed tomography (CT) scans for early diagnosis of lung cancer thanks to the classification of benign and malignant nodules. The proposed pipeline is composed of four stages. In preprocessing steps, CT images are enhanced, and lung volumes are extracted from the image with the help of a novel method called lung volume extraction method (LUVEM). The significance of the proposed pipeline is using LUVEM for extracting lung region. In nodule detection stage, candidate nodules are determined according to the circular Hough transform- (CHT-) based method. Then, lung nodules are segmented with self-organizing maps (SOM). In feature computation stage, intensity, shape, texture, energy, and combined features are used for feature extraction, and principal component analysis (PCA) is used for feature reduction step. In the final stage, probabilistic neural network (PNN) classifies benign and malign nodules. According to the experiments performed on our dataset, the proposed pipeline system can classify benign and malign nodules with 95.91% accuracy, 97.42% sensitivity, and 94.24% specificity. Even in cases of small-sized nodules (3–10 mm), the proposed system can determine the nodule type with 94.68% accuracy.

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Automatic Lung Nodule Detection Using Template Matching

Cited by 17 publications

References 6 publications

Nodule Detection in a Lung Region that's Segmented with Using Genetic Cellular Neural Networks and 3D Template Matching with Fuzzy Rule Based Thresholding

Nodule Detection in a Lung Region that's Segmented with Using Genetic Cellular Neural Networks and 3D Template Matching with Fuzzy Rule Based Thresholding

Multiple Object Detection in Images using Template Matching

A Computer-Aided Pipeline for Automatic Lung Cancer Classification on Computed Tomography Scans

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