Ground glass opacity detection and segmentation using CT images: an image statistics framework
Abstract Lung cancer is one of the most profound causes of cancer‐related deaths in the world. Early detection is known to significantly improve the chances of survival. Several detection and diagnostic methods are used for this purpose. CT is one of the most widely used non‐invasive medical imaging...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-07-01
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| Series: | IET Image Processing |
| Online Access: | https://doi.org/10.1049/ipr2.12498 |
| Summary: | Abstract Lung cancer is one of the most profound causes of cancer‐related deaths in the world. Early detection is known to significantly improve the chances of survival. Several detection and diagnostic methods are used for this purpose. CT is one of the most widely used non‐invasive medical imaging modalities in this domain. The biggest challenge faced by radiologists in this case is detection and diagnosis of cancerous lung nodules. The growth of ground glass opacity (GGO) lesions is an indication of malignancy. However, GGO is difficult to capture for physicians as it manifests in the form of tiny, faint shadows. This research paper proposes an approach for aiding GGO identification in CT lung images for improved lung cancer prognosis. In the proposed approach, morphological reconstruction is used for segmentation. Once the region of interest (ROI) is extracted, statistical analysis using mean, standard deviation, variance, entropy, skewness, kurtosis, minimum grey scale value, maximum grey scale value and range is performed. The same statistical measures are determined for normal lung and distribution plot is drawn for comparison. It is observed that maximum grey‐scale value demonstrates minimum overlap of approximately 7.4%. To reduce this, a joint feature by summing values of feature mean, skewness, and maximum grey‐scale value was used. This approach reduced the overlap to approximately 1.32%. Lastly, ANN was used for classification of GGO and non‐GGO lung tissue with an achieved accuracy of 99.5%. |
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| ISSN: | 1751-9659 1751-9667 |