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Literature DB >> 16140500

Computer-aided diagnosis: a shape classification of pulmonary nodules imaged by high-resolution CT.

Shingo Iwano¹, Tatsuya Nakamura, Yuko Kamioka, Takeo Ishigaki.

Abstract

We investigated the possibility of using computer analysis of high-resolution CT images to radiologically classify the shape of pulmonary nodules. Using a combination of circularity and second moment as quantitative measures we were able to classify pulmonary nodules in each shape group as effectively as could a radiologist. We found that pulmonary nodules with circularity < or =0.75 and second moment < or =0.18 were very likely to reveal lung cancer.

Entities: Disease

Mesh：

Year: 2005 PMID： 16140500 DOI： 10.1016/j.compmedimag.2005.04.009

Source DB: PubMed Journal: Comput Med Imaging Graph ISSN： 0895-6111 Impact factor: 4.790

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Cited

11 in total

1. A Combination of Shape and Texture Features for Classification of Pulmonary Nodules in Lung CT Images.

Authors: Ashis Kumar Dhara; Sudipta Mukhopadhyay; Anirvan Dutta; Mandeep Garg; Niranjan Khandelwal
Journal: J Digit Imaging Date: 2016-08 Impact factor: 4.056

2. Computer-aided diagnosis systems for lung cancer: challenges and methodologies.

Authors: Ayman El-Baz; Garth M Beache; Georgy Gimel'farb; Kenji Suzuki; Kazunori Okada; Ahmed Elnakib; Ahmed Soliman; Behnoush Abdollahi
Journal: Int J Biomed Imaging Date: 2013-01-29

3. Lung nodule malignancy classification using only radiologist-quantified image features as inputs to statistical learning algorithms: probing the Lung Image Database Consortium dataset with two statistical learning methods.

Authors: Matthew C Hancock; Jerry F Magnan
Journal: J Med Imaging (Bellingham) Date: 2016-12-08

Computer-aided diagnosis: a shape classification of pulmonary nodules imaged by high-resolution CT.

1. A Combination of Shape and Texture Features for Classification of Pulmonary Nodules in Lung CT Images.

2. Computer-aided diagnosis systems for lung cancer: challenges and methodologies.

3. Lung nodule malignancy classification using only radiologist-quantified image features as inputs to statistical learning algorithms: probing the Lung Image Database Consortium dataset with two statistical learning methods.

4. The self-overlap method for assessment of lung nodule morphology in chest CT.

5. Texture feature analysis for computer-aided diagnosis on pulmonary nodules.

6. The normal mode analysis shape detection method for automated shape determination of lung nodules.

7. Development of an automatic classification system for differentiation of obstructive lung disease using HRCT.

8. Empirical Driven Automatic Detection of Lobulation Imaging Signs in Lung CT.

9. A Novel Computer-Aided Diagnosis Scheme on Small Annotated Set: G2C-CAD.

10. Classification of pulmonary nodules by using hybrid features.