| Summary: | 碩士 === 中原大學 === 生物醫學工程研究所 === 100 === Abstract
The pancreas is the important organ in the digestive tract of human body. The pancreatic cancer has an extreme mortality, because it is hard to be detected in the early stage for physicians. Therefore, enhanced images that injected contrast medium are often used in the clinical diagnosis. This study will develop a computer-aided diagnosis system by applying image processing to help physicians detect pancreatic cancer in the early stage, and explore the possibility of reducing the patient injected contrast medium.
First of all, the original CT image was preprocessed by using the median filter and wavelet transform. Secondly, histogram equalization and Otsu’s method were used to get the image region of pancreas. And then, the region growing method was used to divide an image into the images of pancreatic tumor and pancreas. Finally, ROI of images was divided into four different sub-images as “the region of pancreas in processed image”, “the region of pancreatic tumor in processed image”, “the region of pancreas in original image” and “the region of pancreatic tumor in original image”. The sub-images are proceeded with textural features analysis and morphological features analysis to explore whether the important physiological information of the image are lost during image processing, and which is the more accurate system to distinguish between the region of pancreas and pancreatic tumor. Totally, 41 enhanced CT images and 36 unenhanced CT images are used as train and test data sets. The features have been chosen with independent T-test and entered into self-organizing map (SOM) to be classified, and then the result of system classified contrast with the pathological result of patients to evaluate the developed system.
The results show as following. 1. For enhanced CT images, using “the region of pancreas in processed image” is the best way to classify the tissue of tumor and pancreas (the first stage classification) with sensitivity=0.973, accuracy=0.873, kappa=0.839, and using “the region of pancreatic tumor in original image” is the best way to classify tumor as benign or malignant (the second stage classification) with sensitivity=0.897, accuracy=0.914, kappa=0.748, respectively. The average time cost for the classification of single image is about 13 seconds. 2. For unenhanced CT images, using “the region of pancreatic tumor in original image” is the best way both in the classification of first and second stage with sensitivity=0.994, accuracy=0.995, kappa=0.984, and sensitivity=0.874, accuracy=0.887, kappa=0.583, respectively. The average time cost for the classification of single image is about 11 seconds. Moreover, the malignant tumor has lower value of “Energy” in the enhanced CT images and the two textural features as “Entropy” and “Energy” are the most notable in the unenhanced CT images on selecting the characteristic interpretations of benign or malignant tumors, which provide physicians as diagnosis reference.
This study has already developed a computer-aided diagnosis system for detecting pancreatic cancer in the early stage, and it could help physicians and provide them second opinion. So far, there are no significant differences for the performance of system for unenhanced and enhanced CT images, however, we still need more data to evaluate and confirm the necessity of injecting medical contrast medium into patients.
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