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.

Author Affiliations +

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

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

;

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
10 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Image processing algorithms and systems

Detection and tracking algorithms

Breast

Image segmentation

Human-machine interfaces

Signal to noise ratio

Algorithm development

Show All Keywords

Keywords/Phrases

Search In:

Publication Years