Abstract
The Haralick texture features are a well-known mathematical method to detect the lung abnormalities and give the opportunity to the physician to localize the abnormality tissue type, either lung tumor or pulmonary edema. In this paper, statistical evaluation of the different features will represent the reported performance of the proposed method. Thirty-seven patients CT datasets with either lung tumor or pulmonary edema were included in this study. The CT images are first preprocessed for noise reduction and image enhancement, followed by segmentation techniques to segment the lungs, and finally Haralick texture features to detect the type of the abnormality within the lungs. In spite of the presence of low contrast and high noise in images, the proposed algorithms introduce promising results in detecting the abnormality of lungs in most of the patients in comparison with the normal and suggest that some of the features are significantly recommended than others.
Highlights
The lung is an organ that performs a multitude of vital functions every second of our lives
Manual or semiautomatic lung segmentation methods for computed tomography (CT) images were used in some early Computer-aided diagnosis (CAD) schemes [6,7,8,9,10], they are impractical for current CAD schemes because multidetector CT (MDCT)
For each lung CT image, we separate the left lung from the right lung automatically, and each separated lung is labeled as normal or edema/cancer based on the dataset information
Summary
The lung is an organ that performs a multitude of vital functions every second of our lives. This fact leads to considering lung abnormalities, life-sustained diseases that have high priority in detection, diagnosis, and treatment if possible. Our focus in this paper will be on two popular abnormalities within the lung, which are pulmonary edema and lung tumor. Computer-aided diagnosis (CAD) schemes for thoracic computed tomography (CT) are widely used to characterize, quantify, and detect numerous lung abnormalities, such as pulmonary edema and lung cancer [4, 5]. An accurate lung segmentation method is always a critical first step in these CAD schemes and can significantly improve the performance level of these schemes. Manual or semiautomatic lung segmentation methods for CT images were used in some early CAD schemes [6,7,8,9,10], they are impractical for current CAD schemes because multidetector CT (MDCT)
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
