Multi-Resolution Fuzzy Tumor Detection for PC-based Breast Ultrasound
Date Issued
2008
Date
2008
Author(s)
Chien, Chih-Hsuan
Abstract
Breast cancer is the most frequently diagnosed cancer in women all over the world. The early detection of the breast cancer provides a better chance of proper treatment. There are many technologies for diagnosis, such as ultrasound (US) and X-ray. In general, US is the efficient method used for breast cancer detection and diagnose presently and has less injury to human body. In recent years, the portable PC-based US imaging systems developed by some companies can provide an integrated computer environment for the computer-aided diagnosis and detection applications. In this paper, an automatic tumor detection system based on the fuzzy approach using the PC-based US system Terason t3000 (Terason Ultrasound, Burlington, MA, USA) is proposed. In order to easily retrieve the US images for any regions of the breast, a clock-based storing system is proposed to store the scanned US images. A computer-aided detection (CAD) system is also included to save the physicians’ time for a huge volume of scanned US images. The multi-resolution technique is also applied in the CAD system for detecting tumors with different sizes. First, because the differences between the successive US images are unobvious generally; several US images will be overlapped together for improving the image quality and reducing the detection time. Then, the overlapped images will be resampled to three kinds of image resolutions for improving the detection accuracy. Because of the noise in the US image, some preprocessing techniques, such as smoothing filter and sigmoid filter, are used to reduce noise and enhance the object boundary. Then, the fuzzy technique is applied to detect tumors in the images with different resolutions, respectively. After the detecting tumor in the multiple resolution images, the pre-defined criteria evaluation is applied to remove the unreasonable tumors in order to reduce the false positives. Finally, the relative relationships of suspicious regions in multiple images with different resolutions can be compared to each other for obtaining the final detection results. Moreover, the free-response operative characteristics (FROC) curve is used to evaluate the detection performance of the proposed system. According to the experimental results of 60 cases, the proposed system yields a 91.8% detection sensitivity at the 1.67 false positives per case.
Subjects
fuzzy；ultrasound；multi-resolution
SDGs
Type
thesis
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