Automatic Tumor Detection for Dynamic Contrast-Enhanced Magnetic Resonance Imaging
Date Issued
2014
Date
2014
Author(s)
Huang, Yan-Hao
Abstract
Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is technique to form images according to the variation information of concentration of the contrast agent, which shows different enhancement in different kinds of tissues. A four-dimensional (4-D) image from each patient is acquired by scanning patient at different time and a 4-D image contains dozens of three-dimensional (3-D) images. In recent studies, DCE-MRI is the most sensitive tool to detect breast cancer by radiologists for clinical practice. Because an acquisition of 3-D DCE-MRI consists of dozens of 2-D images, it is time-consuming to find the tumor or diagnose the cancer from such large amount of images by physicians and the misdetection might occur due to physicians’ fatigues. In order to make inspection of DCE-MRI images more efficient, computer-aided detection (CADe) systems have been proposed to interpret the images. CADe can not only shorten the interpretation time but also probably detect the tumor which could be not found by physicians. However, it is difficult to implement a CADe system to detect tumors efficiently from a large number of 3-D DCE-MRI images. To date, no related studies for development of CADe systems on DCE-MRI images have been reported to detect breast lesions. In the thesis, we proposed two CADe systems for DCE-MRI images. In the first study, the tumor detection based on the two-stage technique was applied to distinguish breast masses from normal tissues using binary logistic regression. The tumor regions which consist of enhanced tissues were detected at first stage and then the second stage were combined to identify the suspected regions which could not be identified easily. The results from two-stage detection algorithm were considered as the detected masses. In the second study, the tumor detection based on fuzzy c-mean (FCM) was proposed to locate enhanced regions firstly on DCE-MRI. Because of the blob-like characteristic of breast mass, we detected masses from the enhanced regions by the Hessian method. The results showed that the two CADe systems have high detection performance for the breast tumor detection on DCE-MRI images.
Subjects
乳癌
電腦輔助偵測系統
動態對比增強磁振造影
兩階段式
海森
SDGs
Type
thesis
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