| Paper: | MP-P6.11 |
| Session: | Biomedical Imaging I |
| Time: | Monday, September 17, 14:30 - 17:10 |
| Presentation: |
Poster
|
| Title: |
A NEW CAD SYSTEM FOR EARLY DIAGNOSIS OF DETECTED LUNG NODULES |
| Authors: |
Ayman El-Baz; University of Louisville | | |
| | Georgy Gimelfarb; University of Auckland | | |
| | Robert Falk; Jewish Hospital | | |
| | Mohamed Abo El-Ghar; University of Mansoura | | |
| Abstract: |
A pulmonary nodule is the most common manifestation of lung cancer. Lung nodules are approximately-spherical regions of relatively high density that are visible in X-ray images of the lung. Large (generally defined as greater than 1 cm in diameter) malignant nodules can be easily detected with traditional imaging equipment and can be diagnosed by needle biopsy or bronchoscopy techniques. However, the diagnostic options for small malignant nodules are limited due to problems associated with accessing small tumors, especially if they are located deep in the tissue or away from the large airways; therefore, additional diagnostic and imaging techniques are needed. One of the most promising techniques for detecting small cancerous nodules relies on characterizing the nodule based on its growth rate. The growth rate is estimated by measuring the volumetric change of the detected lung nodules over time, so it is important to accurately measure the volume of the nodules to quantify their growth rate over time. In this paper, we introduce a novel Computer Assisted Diagnosis (CAD) system for early diagnosis of lung cancer. The proposed CAD system consists of five main steps. These steps are: i) segmentation of lung tissues from low dose computed tomography (LDCT) images, ii) detection of lung nodules from segmented lung tissues, iii) a non-rigid registration approach to align two successive LDCT scans and to correct the motion artifacts caused by breathing and patient motion, iv) segmentation of the detected lung nodules, and v) quantification of the volumetric changes. Our preliminary classification results based on the analysis of the growth rate of both benign and malignant nodules for 10 patients (6 patients diagnosed as malignant and 4 diagnosed as benign) were 100% for 95% confidence interval. The preliminary results of the proposed image analysis have yielded promising results that would supplement the use of current technologies for diagnosing lung cancer. |
