Korean J Radiol.  2005 Dec;6(4):221-228. 10.3348/kjr.2005.6.4.221.

Mammographic Mass Detection Using a Mass Template

• Ozekes S
• Osman O
• Camurcu AY

• KMID: 1777279
• DOI: http://doi.org/10.3348/kjr.2005.6.4.221

Abstract

OBJECTIVE
The purpose of this study was to develop a new method for automated mass detection in digital mammographic images using templates. MATERIALS AND METHODS: Masses were detected using a two steps process. First, the pixels in the mammogram images were scanned in 8 directions, and regions of interest (ROI) were identified using various thresholds. Then, a mass template was used to categorize the ROI as true masses or non-masses based on their morphologies. Each pixel of a ROI was scanned with a mass template to determine whether there was a shape (part of a ROI) similar to the mass in the template. The similarity was controlled using two thresholds. If a shape was detected, then the coordinates of the shape were recorded as part of a true mass. To test the system's efficiency, we applied this process to 52 mammogram images from the Mammographic Image Analysis Society (MIAS) database. RESULTS: Three hundred and thirty-two ROI were identified using the ROI specification methods. These ROI were classified using three templates whose diameters were 10, 20 and 30 pixels. The results of this experiment showed that using the templates with these diameters achieved sensitivities of 93%, 90% and 81% with 1.3, 0.7 and 0.33 false positives per image respectively. CONCLUSION: These results indicate that the detection performance of this template based algorithm is satisfactory, and may improve the performance of computer-aided analysis of mammographic images and early diagnosis of mammographic masses.

Keyword

Mass detection; Computer aided detection; Mammography

MeSH Terms

Sensitivity and Specificity
Radiographic Image Enhancement
Mammography/*methods
Humans
False Positive Reactions
Automation
Algorithms

Figure

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  Fig. 1 A. Mammogram with a circumscribed mass. B. Mammogram with a spiculated mass.

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  Fig. 2 Minimum and maximum distance thresholds in 8 directions.

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  Fig. 3 A. A pixel which does not have a number of adjacent neighbor pixels greater than or equal to the "minimum distance threshold" value, which means it is not a part of the ROI. B. A pixel which does not have a number of adjacent neighbor pixels less than or equal to the "maximum distance threshold" value, which means it is not a part of the ROI. C. A pixel which has a number of adjacent neighbor pixels greater than or equal to the "minimum distance threshold" value, and less than or equal to the "maximum distance threshold" value, which means it is a part of the ROI.

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  Fig. 4 The mass template with dimensions 30×30 pixels.

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  Fig. 5 A. A mammographic mass. B. Detecting part of a mass using the template.

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  Fig. 6 A. A blood vessel. B. Detecting part of a blood vessel using the template.

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  Fig. 7 A. The mammogram with a circumscribed mass. B. The specified ROI. C. The detected circumscribed mass.

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  Fig. 8 A. The mammogram with a spiculated mass. B. The specified ROI. C. The detected spiculated mass.

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  Fig. 9 Curve of the true-positive regions per image versus the template diameters.

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  Fig. 10 Curve of the false-positive regions per image versus the template diameters.

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  Fig. 11 Free-response receiver operating characteristic curve showing the performance of the mass detection task.

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