Lesion Detection based on Annotation of Anatomic Structures in Breast Sonograms
Date Issued
2009
Date
2009
Author(s)
Chi, Chen-Ning
Abstract
Breast cancer is one of the most frequent types of cancer found in females, and it has the highest incident rate of all cancers among females over the age of 40. Therefore, it is important to detect and treat it at an early stage.here are several breast cancer diagnosis techniques, like mammography, magnetic resonance imaging (MRI), sonograms (ultrasound images), etc. With the benefits of ultrasound, convenience, non-invasiveness and relatively low cost, ultrasound images are considered as useful information on the screening of females at high risk for breast cancer.ltrasound breast cancer screening technique plays an important role in the field of the breast diagnosis. Ultrasound breast cancer screening technique has progressed from a 2D single image to a 3D volume data image which is composed of hundreds of 2D images.utomatic detection of breast lesions in a series of 2D breast sonograms is great help for breast cancer screening; and thus the development of computer-aided detection (CAD) is needed. It provides a convenient way for doctors and radiologists to detect breast cancer while using ultrasound images. A lot of methods for computer-aided detection systems using ultrasound images have been developed by many researchers around the world.hile several approaches have been proposed previously, the false positive rate still tends to be too high for practical use because of the sonographic artifacts. One possible way to reduce the false positive rate is to incorporate the anatomic information into theecision-making strategy. For anatomic information, most lesions appearing in breast tissue are located in-between the fat layer and the muscle layer in sonograms In this thesis, we present a new detection algorithm for identification of the fat and muscle layers first, and use the in-between region of those two layers to detect lesions.or muscle layer detection, the basic idea is to identify the area with rich horizontal strip texture patterns by a newly developed texture descriptor computed by the Gabor filters and phase symmetry techniques. For fat layer detection, an automatic thresholding approach in collaboration with a topological relation is proposed. Once the fat and muscle layers are determined, the hypoechoic regions in-between these two layers are more likely to be a breast lesion.nalysis of anatomic information of breast may increase the accuracy of lesion detection in a single breast ultrasound image. Having examining 60 ultrasound test cases, almost all the cysts and lesions could be detected successfully. The experimental results prove the accuracy of the proposed algorithm.
Subjects
Breast sonograms
computer-aided detection
breast anatomic information
muscle detection
fat detection
automatic lesion detection
SDGs
Type
thesis
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