Lesion identification from scintimammography breast images

Niranjan Tallapally; Ramakrishnan Sundaram; Leonard R. Coover M.D.

doi:10.1117/12.473888

9 June 2003 Lesion identification from scintimammography breast images

Niranjan Tallapally, Ramakrishnan Sundaram, Leonard R. Coover M.D.

Proceedings Volume 5009, Visualization and Data Analysis 2003; (2003) https://doi.org/10.1117/12.473888
Event: Electronic Imaging 2003, 2003, Santa Clara, CA, United States

Abstract

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. LumaGEM^TM 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.

Citation Download Citation

Niranjan Tallapally, Ramakrishnan Sundaram, and Leonard R. Coover M.D. "Lesion identification from scintimammography breast images", Proc. SPIE 5009, Visualization and Data Analysis 2003, (9 June 2003); https://doi.org/10.1117/12.473888

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KEYWORDS

Image processing algorithms and systems

Detection and tracking algorithms

Breast

Image segmentation

Human-machine interfaces

Signal to noise ratio

Algorithm development

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