The Development of Computer Aided Diagnosis System for Pancreatic Tumor in Ultrasound Image

• Main Authors: Chia-Chen Wu, 吳佳宸
• Other Authors: Jenn-Lung Su
• Format: Others
• Language: zh-TW
• Published: 2009
• Online Access: http://ndltd.ncl.edu.tw/handle/73607633441862625555

碩士 === 中原大學 === 醫學工程研究所 === 97 === The pancreas is one of the important gastrointestinal tract organs. Due to physiological limitations, a physician is hard to make an accurate diagnosis for patients, although the pancreatic cancer had an extreme mortality. The abdominal ultrasound is the most popular way for making a diagnosis; however, a noisy ultrasound image will reduce its overall diagnostic efficiency. The main purpose of this study is to develop a computer-aided diagnosis (CAD) system for pancreatic tumors in ultrasound images to provide a physician some diagnosis information. In this study, after reducing noises, enhancing contrast, and detecting boundary in the original ultrasound image, an entire contour of a tumor was segmented, and its real area and perimeter was calculated to analyze texture and morphological features for this image. After evaluating the results by the independent T-test, the effective features were selected and severd as inputs in the self-organizing map (SOM) which fixed with different modes to classify the ultrasound images. The diagnostic efficiency of this CAD system was evaluated after comparing the classified results of ultrasound images with the pathological results of patients. Totally 40 pancreatic ultrasound images which included 26 pancreatic tumor images (abnormal data) and 14 normal pancreas images (normal data) were respectively used to develop and evaluate this CAD system. Besides, the 26 pancreatic tumor images included 9 benign tumor images (benign data) and 17 malignant tumor images (malignant data). The primary results showed that this CAD system had a better performance by dividing into the training and testing groups than leave-one-out, and its accuracy and sensitivity both were 1 for classifying an ultrasound image as normal or abnormal. The diagnostic efficiency of this CAD system did not affect by grouping when it used 6 reference neurons to classify ultrasound images. When this CAD system classified a pancreatic tumor image as benign or malignant, its accuracy and sensitivity were 0.8462 and 1, respectively. Moreover, Sensitivity of this CAD system was increased when it used 4 reference neurons to classify the pancreatic tumor images. Morphological features had a better performance than texture features for tumor classification in a pancreatic tumor image. In this study, 8 features were proved to classify normal data and abnormal data effectively, and 4 of them were also proved to classify benign data and malignant data effectively. The area of a tumor was the most important morphological feature for tumor classification. A benign pancreatic tumor usually had a smaller area and a smoother contour than a malignant pancreatic tumor. The average time cost for this CAD system is 25 seconds to evaluate an ultrasound image. In this study, the CAD system which combined image enhancement with feature analysis was developed. It could help a physician make a diagnosis, and decreased the probability of making an incorrect or an invasive diagnosis for patients. The CAD system could combine with other medical imaging to make a more complete evaluation tool for pancreatic tumors in the future.