A novel supervised approach for segmentation of lung parenchyma from chest CT for computer-aided diagnosis
| Autor: | Shiloah Elizabeth Darmanayagam, Khanna Nehemiah Harichandran, Kannan Arputharaj, Sunil Retmin Raj Cyril |
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| Rok vydání: | 2012 |
| Předmět: |
Lung Diseases
medicine.medical_specialty Sensitivity and Specificity Article Lung Disorder Parenchyma medicine Humans Radiology Nuclear Medicine and imaging Segmentation Diagnosis Computer-Assisted Bronchiectasis Lung Radiological and Ultrasound Technology business.industry respiratory system medicine.disease Thresholding Computer Science Applications respiratory tract diseases Pneumonia medicine.anatomical_structure Computer-aided diagnosis Radiographic Image Interpretation Computer-Assisted Radiology Neural Networks Computer business Tomography X-Ray Computed Algorithms |
| Zdroj: | Journal of digital imaging. 26(3) |
| ISSN: | 1618-727X |
| Popis: | Segmentation of lung parenchyma from the chest computed tomography is an important task in analysis of chest computed tomography for diagnosis of lung disorders. It is a challenging task especially in the presence of peripherally placed pathology bearing regions. In this work, we propose a segmentation approach to segment lung parenchyma from chest. The first step is to segment the lungs using iterative thresholding followed by morphological operations. If the two lungs are not separated, the lung junction and its neighborhood are identified and local thresholding is applied. The second step is to extract shape features of the two lungs. The third step is to use a multilayer feed forward neural network to determine if the segmented lung parenchyma is complete, based on the extracted features. The final step is to reconstruct the two lungs in case of incomplete segmentation, by exploiting the fact that in majority of the cases, at least one of the two lungs would have been segmented correctly by the first step. Hence, the complete lung is determined based on the shape and region properties and the incomplete lung is reconstructed by applying graphical methods, namely, reflection and translation. The proposed approach has been tested in a computer-aided diagnosis system for diagnosis of lung disorders, namely, bronchiectasis, tuberculosis, and pneumonia. An accuracy of 97.37 % has been achieved by the proposed approach whereas the conventional thresholding approach was unable to detect peripheral pathology-bearing regions. The results obtained prove to be better than that achieved using conventional thresholding and morphological operations. |
| Databáze: | OpenAIRE |
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