Image segmentation plays a crucial role in detecting cancerous lesions in breast images. Typically, the images obtained are large in dimension and it will take considerable time to run traditional image segmentation algorithms to detect and localize lesions. To increase the efficiency of the detection process, this paper develops an efficient image segmentation algorithm which limits its attention to regions where there is the possibility of lesions to exist. The image segmentation algorithm is then applied to these regions to find a threshold value. There are three primary objectives of this paper. First, to design and implement a region of interset algorithm known as the Ranking algorithm. Secondly, to identify whether the regions detected are linked using the Linkage algorithm. Thirdly, to apply the image segmentation algorithm (Otsu algorithm) to these regions to obtain a threshold value. This threshold value is then used for global image segmentation.
Proc. SPIE. 5009, Visualization and Data Analysis 2003
KEYWORDS: Image processing algorithms and systems, Signal to noise ratio, Breast, Human-machine interfaces, Detection and tracking algorithms, Visualization, Cameras, Image segmentation, Spatial resolution, Algorithm development
The identification and localization of lesions in scintimammography breast images is a crucial stage in the early detection of cancer. Scintimammography breast images are obtained using a small, high-resolution breast-specific Gamma Camera (e.g. LumaGEMTM Gamma Ray Camera, Gamma Medica Instruments, Northridge, CA). The resulting images contain information about possible lesions but they are very noisy. This requires a robust image segmentation algorithm to accurately contour the region should it exist. The algorithm must perform robust localization, minimize the misclassifications, and lead to efficient practical implemetations despite the influence of blurring and the presence of noise. This paper discusses and implements a robust spatial domain algorithm known as the Otsu algorithm for automatic selection of threshold level from the image histogram and to detect and contour objects/regions in grayscale digital images. Specifically, this paper develops the algorithm that is used to identify cancerous lesions in breast images. There are two primary objectives of this paper. First, to design and implement a contour detection algorithm suitable for the constraints posed by scintimammography breast images, and secondly, to provide the physician with a Graphical User Interface (GUI) which facilitates the visualization and classification of the images.