Related Experiment Video
Updated: May 14, 2026

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
Published on: August 30, 2013
A new ROC analysis method considering the correlation between neighboring pixels
1Medical Imaging Research Center, Illinois Institute of Technology, Chicago, IL, USA.
Abstract:
In this paper, we introduce a novel receiver operating characteristic (ROC) analysis method that considers spatial correlation between pixels to evaluate classification algorithms. ROC analysis is one of the most important tools in the evaluation of medical images and computer aided diagnosis (CAD) systems. It provides a comprehensive description of the detection accuracy of the test image. To evaluate the localization performance, operating points of ROC curves are obtained based on the classification results of individual pixels. To this date, the confidence level or intensity value of each pixel is assumed to be independent within the image. However, this assumption is not satisfied in real problems. In this paper, a new ROC analysis algorithm that considers the correlation between neighboring pixels is proposed. Our results show that the new ROC curves provide a more accurate evaluation of the test image.
Related Concept Videos
Region of Convergence of Laplace Tarnsform
Consider a decaying exponential signal that begins at a specific time. When deriving its Laplace transform, the time-domain variable is replaced with a complex variable. This substitution...
Receiver Operating Characteristic Plot
Region of Convergence
Correlation
Two variables, for example, a and b, are said to be positively correlated if both variables move in the same direction. In other words, a positive correlation exists between two variables, a and b, if:
Area Computation by the Alternative Coordinate Method
Coefficient of Correlation
If you suspect a linear relationship between x and y, then r can measure how strong the linear relationship is.
What the VALUE of r tells us:
The value of r is always between –1 and +1: –1 ≤ r ≤ 1.
The size of the correlation r indicates the strength of the linear...