Article: Ground glass opacity lesion morphology extraction in primary lung cancer Journal: International Journal of Medical Engineering and Informatics (IJMEI) 2017 Vol.9 No.4 pp.398 - 411 Abstract: In determining the level of tumour malignancy in lung cancer, several characteristics of lesion in the lungs need to be recognised. The characteristics include several components, namely tumour size, enhancement, irregular spiculated edge, lobulated, air bronchograms, ground glass opacity (GGO) and density. This study identifies GGO lesion characteristics using CT image datasets obtained from Sardjito Public Hospital, Indonesia. The initial stage conducted is a cropping process performed by a radiologist so that the research's focus is merely on the lesion. The next process is the feature extraction by using grey level co-occurrence matrices (GLCM) with four features, namely energy, contrast, correlation and homogeneity. The classification stage is carried out after the extraction stage which is followed by features selection. Having selected two most dominant features from total of 16 features, the proposed method achieves accuracy of 88.8%, sensitivity of 87.5% and specificity of 90%. Inderscience Publishers - linking academia, business and industry through research

Title: Ground glass opacity lesion morphology extraction in primary lung cancer

Authors: H.A. Nugroho; M.M. Sebatubun; T.B. Adji

Addresses: Department of Electrical Engineering and Information Technology, Faculty of Engineering, Universitas Gadjah Mada, Jl. Grafika 2, Kampus UGM, Yogyakarta 55281, Indonesia ' Department of Electrical Engineering and Information Technology, Faculty of Engineering, Universitas Gadjah Mada, Jl. Grafika 2, Kampus UGM, Yogyakarta 55281, Indonesia ' Department of Electrical Engineering and Information Technology, Faculty of Engineering, Universitas Gadjah Mada, Jl. Grafika 2, Kampus UGM, Yogyakarta 55281, Indonesia

Abstract: In determining the level of tumour malignancy in lung cancer, several characteristics of lesion in the lungs need to be recognised. The characteristics include several components, namely tumour size, enhancement, irregular spiculated edge, lobulated, air bronchograms, ground glass opacity (GGO) and density. This study identifies GGO lesion characteristics using CT image datasets obtained from Sardjito Public Hospital, Indonesia. The initial stage conducted is a cropping process performed by a radiologist so that the research's focus is merely on the lesion. The next process is the feature extraction by using grey level co-occurrence matrices (GLCM) with four features, namely energy, contrast, correlation and homogeneity. The classification stage is carried out after the extraction stage which is followed by features selection. Having selected two most dominant features from total of 16 features, the proposed method achieves accuracy of 88.8%, sensitivity of 87.5% and specificity of 90%.

Keywords: CT image; grey level co-occurrence matrices; GLCM; ground glass opacity; GGO; lung cancer.

DOI: 10.1504/IJMEI.2017.086899

International Journal of Medical Engineering and Informatics, 2017 Vol.9 No.4, pp.398 - 411

Received: 27 Oct 2016
Accepted: 24 Nov 2016
Published online: 02 Oct 2017 *

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Title: Ground glass opacity lesion morphology extraction in primary lung cancer

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