Automatic detection of solitary lung nodules using quality threshold clustering, genetic algorithm and diversity index

Filho, Antônio Oséas de Carvalho; Sampaio, Wener Borges de; Silva, Aristófanes Corrêa; Paiva, Anselmo Cardoso de; Nunes, Rodolfo Acatauassú; Gattass, Marcelo

doi:10.1016/j.artmed.2013.11.002

Cited by 100 publications

(37 citation statements)

References 16 publications

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“…As can be inferred from Table 1, the proposed approach outperforms the other competing methods except the method which is based on threshold clustering and genetic algorithm (de Carvalho et al, 2014). The me-thod based on the crawlers features and SVM (Froz et al, 2017) is the third ranked as well.…”

Section: Discussionmentioning

confidence: 86%

“…The average detection accuracy of our approach is about 90.0% for ELCAP dataset and about 95.1% for LIDC-IRDI dataset. For the methods proposed in (de Carvalho et al, 2014) and (Froz et al, 2017), this ratio is 97.6% and 94.3% for LIDC-IRDI case, respectively. However, the proposed method excels the others in terms of detection sensitivity.…”

Section: Discussionmentioning

confidence: 95%

“…Along with the proposed method, Table 1 contains the results of applying nine other nodule detection methods to similar datasets. The competing methods are based on Pixon-based segmentation (Hassanpour et al, 2011), template matching and neural classifier (Hasanabadi et al, 2014), hybrid features (Akram et al, 2016), Level-Set method (Silveira et al, 2007), a method based on genetic algorithm (GA) and SRM (Zehtabian and Ghassemian, 2016b), context curve calculation (Zhang et al, 2013), circular features based method (Mousa and Khan, 2002), a method based on threshold clustering and GA (de Carvalho et al, 2014), and a method based on artificial crawlers feature extraction and SVM (Froz et al, 2017). Among the competing methods, the first five methods have been implemented again by the authors of the present article on the new dataset(s), but under similar circumstances and conditions.…”

Section: Resultsmentioning

confidence: 99%

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Automatic Lung Nodule Detection Based on Statistical Region Merging and Support Vector Machines

2017

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Lung cancer is one of the most common diseases in the world that can be treated if the lung nodules are detected in their early stages of growth. This study develops a new framework for computer-aided detection of pulmonary nodules thorough a fully-automatic analysis of Computed Tomography (CT) images. In the present work, the multi-layer CT data is fed into a pre-processing step that exploits an adaptive diffusion-based smoothing algorithm in which the parameters are automatically tuned using an adaptation technique. After multiple levels of morphological filtering, the Regions of Interest (ROIs) are extracted from the smoothed images. The Statistical Region Merging (SRM) algorithm is applied to the ROIs in order to segment each layer of the CT data. Extracted segments in consecutive layers are then analyzed in such a way that if they intersect at more than a predefined number of pixels, they are labeled with a similar index. The boundaries of the segments in adjacent layers which have the same indices are then connected together to form three-dimensional objects as the nodule candidates. After extracting four spectral, one morphological, and one textural feature from all candidates, they are finally classified into nodules and non-nodules using the Support Vector Machine (SVM) classifier. The proposed framework has been applied to two sets of lung CT images and its performance has been compared to that of nine other competing state-of-the-art methods. The considerable efficiency of the proposed approach has been proved quantitatively and validated by clinical experts as well.

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Section: Discussionmentioning

confidence: 86%

Section: Discussionmentioning

confidence: 95%

Section: Resultsmentioning

confidence: 99%

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Automatic Lung Nodule Detection Based on Statistical Region Merging and Support Vector Machines

2017

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“…The first factor is related to exams that do not present nodules equal to or larger than 3 mm. The second factor is the divergence of information found in the marking file of an exam versus the information present in the DICOM header of the same exam, which invalidates the marking [Carvalho Filho et al 2014]. Therefore, the proposed methodology was applied to 833 exams.…”

Section: Image Acquisitionmentioning

confidence: 99%

Classification of malignancy of lung nodules in CT images using Convolutional Neural Network

Silva¹,

Silva²,

Paiva³

et al. 2016

Anais Do XVI Simpósio Brasileiro De Computação Aplicada À Saúde (SBCAS 2016)

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Lung cancer presents the highest mortality rate, besides being one of the smallest survival rates after diagnosis. Thereby, early detection is extremely important for the diagnosis and treatment. This paper proposes three different architectures of Convolutional Neural Network (CNN), which is a deep learning technique, for classification of malignancy of lung nodules without computing the morphology and texture features. The methodology was tested onto the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI), with the best accuracy of 82.3%, sensitivity of 79.4% and specificity 83.8%.

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“…de Carvalho et al [6] presented an application of the QT algorithm for the segmentation of structures that resemble a lung nodule. That work showed that the QT was able to detect up to 95 % of the lung nodules in the exam database that was used.…”

Section: Related Workmentioning

confidence: 99%

Automatic Detection of Masses in Mammograms Using Quality Threshold Clustering, Correlogram Function, and SVM

et al. 2014

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Breast cancer is the second most common type of cancer in the world. Several computer-aided detection and diagnosis systems have been used to assist health experts and to indicate suspect areas that would be difficult to perceive by the human eye; this approach has aided in the detection and diagnosis of cancer. The present work proposes a method for the automatic detection of masses in digital mammograms by using quality threshold (QT), a correlogram function, and the support vector machine (SVM). This methodology comprises the following steps: The first step is to perform preprocessing with a low-pass filter, which increases the scale of the contrast, and the next step is to use an enhancement to the wavelet transform with a linear function. After the preprocessing is segmentation using QT; then, we perform post-processing, which involves the selection of the best mass candidates. This step is performed by analyzing the shape descriptors through the SVM. For the stage that involves the extraction of texture features, we used Haralick descriptors and a correlogram function. In the classification stage, the SVM was again used for training, validation, and final test. The results were as follows: sensitivity 92.31 %, specificity 82.2 %, accuracy 83.53 %, mean rate of false positives per image 1.12, and area under the receiver operating characteristic (ROC) curve 0.8033. Breast cancer is notable for presenting the highest mortality rate in addition to one of the smallest survival rates after diagnosis. An early diagnosis means a considerable increase in the survival chance of the patients. The methodology proposed herein contributes to the early diagnosis and survival rate and, thus, proves to be a useful tool for specialists who attempt to anticipate the detection of masses.

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Automatic detection of solitary lung nodules using quality threshold clustering, genetic algorithm and diversity index

Cited by 100 publications

References 16 publications

Automatic Lung Nodule Detection Based on Statistical Region Merging and Support Vector Machines

Automatic Lung Nodule Detection Based on Statistical Region Merging and Support Vector Machines

Classification of malignancy of lung nodules in CT images using Convolutional Neural Network

Automatic Detection of Masses in Mammograms Using Quality Threshold Clustering, Correlogram Function, and SVM

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