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Updated: Mar 23, 2026

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

Multi-scale texture-based level-set segmentation of breast B-mode images.

Itai Lang¹, Miri Sklair-Levy², Hedva Spitzer¹

¹School of Electrical Engineering, Iby and Aladar Fleischman Faculty of Engineering, Tel-Aviv University, Tel-Aviv 69978, Israel.

Computers in Biology and Medicine

|March 25, 2016

Summary

This summary is machine-generated.

This study introduces a novel algorithm for segmenting spiculated breast lesions in ultrasound images, achieving highly accurate boundary detection. The new method significantly improves lesion segmentation, aiding in distinguishing between malignant and benign tumors.

Keywords:

B-mode scan Breast ultrasound imaging Level-set framework Multi-scale Segmentation

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Area of Science:

Medical Imaging
Computational Biology
Artificial Intelligence

Background:

Automatic segmentation of breast lesions in B-mode ultrasound images is challenging due to lesion spiculations and image variability.
Existing computational methods like Markov random fields, neural networks, and level-set techniques have limitations in accurately capturing lesion boundaries.

Purpose of the Study:

To develop an advanced algorithm for precise automatic segmentation of ultrasonographic breast lesions, focusing on capturing spiculated boundaries.
To evaluate the algorithm's performance using established metrics and a novel evaluation method involving radiologist corrections.
To assess the potential of the segmentation results in differentiating between malignant and benign breast lesions.

Main Methods:

Developed a novel algorithm integrating a multi-scale texture identifier, inspired by visual system models, within a level-set framework.
Employed contour-based and region-based error metrics for quantitative performance evaluation.
Introduced a new evaluation method comparing algorithm-generated contours to radiologist-corrected contours.

Main Results:

Achieved a mean absolute error of 0.5 pixels between original and radiologist-corrected contours.
Demonstrated a 99.2% overlapping area between lesion regions segmented by the algorithm and the corrected contours.
Results significantly outperformed previously reported methods in breast lesion segmentation.

Conclusions:

The developed algorithm accurately captures spiculated breast lesion boundaries in ultrasound images.
The novel segmentation approach shows significant potential for improving the discrimination between malignant and benign breast lesions.
This advancement offers a promising tool for enhancing diagnostic accuracy in breast ultrasound analysis.