Automatic Solitary Lung Nodule Detection in Computed Tomography Images Slices

• Published in: Journal of physics. Conference series 2018-01, Vol.953 (1), p.12068
• Main Authors: Sentana, I W B, Jawas, N, Asri, S A
• Format: Article
• Language: English
• Subjects: Algorithms; Computed tomography; Computer vision; Format; Image acquisition; Image analysis; Image classification; Image segmentation; Medical imaging; Physics; Support vector machines; Tomography
• ISSN: 1742-6588; 1742-6596
• DOI: 10.1088/1742-6596/953/1/012068

Lung nodule is an early indicator of some lung diseases, including lung cancer. In Computed Tomography (CT) based image, nodule is known as a shape that appears brighter than lung surrounding. This research aim to develop an application that automatically detect lung nodule in CT images. There are some steps in algorithm such as image acquisition and conversion, image binarization, lung segmentation, blob detection, and classification. Data acquisition is a step to taking image slice by slice from the original *.dicom format and then each image slices is converted into *.tif image format. Binarization that tailoring Otsu algorithm, than separated the background and foreground part of each image slices. After removing the background part, the next step is to segment part of the lung only so the nodule can localized easier. Once again Otsu algorithm is use to detect nodule blob in localized lung area. The final step is tailoring Support Vector Machine (SVM) to classify the nodule. The application has succeed detecting near round nodule with a certain threshold of size. Those detecting result shows drawback in part of thresholding size and shape of nodule that need to enhance in the next part of the research. The algorithm also cannot detect nodule that attached to wall and Lung Chanel, since it depend the searching only on colour differences.