Searching similar images for classification of pulmonary nodules in three-dimensional CT images

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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Three-dimensional computer-aided diagnosis schemes for classification of benign and malignant pulmonary nodules

CARS 2002 Computer Assisted Radiology and Surgery ◽

10.1007/978-3-642-56168-9_128 ◽

2002 ◽

pp. 764-769 ◽

Cited By ~ 1

Author(s):

Y. Kawata ◽

N. Niki ◽

H. Ohmatsu ◽

M. Kusumoto ◽

R. Kakinuma ◽

...

Keyword(s):

Three Dimensional ◽

Pulmonary Nodules ◽

Computer Aided Diagnosis ◽

Computer Aided ◽

Aided Diagnosis

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Hybrid Scheme for Automated Classification of Pulmonary Nodules Using PET/CT Images and Patient Information

Applied Sciences ◽

10.3390/app10124225 ◽

2020 ◽

Vol 10 (12) ◽

pp. 4225

Author(s):

Ayumi Yamada ◽

Atsushi Teramoto ◽

Masato Hoshi ◽

Hiroshi Toyama ◽

Kazuyoshi Imaizumi ◽

...

Keyword(s):

Patient Information ◽

Pulmonary Nodules ◽

Ct Images ◽

Machine Learning Algorithms ◽

Automated Classification ◽

Hybrid Scheme ◽

Laboratory Test Results ◽

Pet Ct ◽

Functional Features

The classification of pulmonary nodules using computed tomography (CT) and positron emission tomography (PET)/CT is often a hard task for physicians. To this end, in our previous study, we developed an automated classification method using PET/CT images. In actual clinical practice, in addition to images, patient information (e.g., laboratory test results) is available and may be useful for automated classification. Here, we developed a hybrid scheme for automated classification of pulmonary nodules using these images and patient information. We collected 36 conventional CT images and PET/CT images of patients who underwent lung biopsy following bronchoscopy. Patient information was also collected. For classification, 25 shape and functional features were first extracted from the images. Benign and malignant nodules were identified using machine learning algorithms along with the images’ features and 17 patient-information-related features. In the leave-one-out cross-validation of our hybrid scheme, 94.4% of malignant nodules were identified correctly, and 77.7% of benign nodules were diagnosed correctly. The hybrid scheme performed better than that of our previous method that used only image features. These results indicate that the proposed hybrid scheme may improve the accuracy of malignancy analysis.

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