Diagnosis of breast tumors with sonographic texture analysis using run-length matrix‏

To explore the potential of texture analysis based on run-length matrix features for classifying benign and malignant breast tumors in ultrasound imaging...

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Abstract

Background:

Early detection and reliable diagnosis of breast cancer could lead to improved cure rates and reduce mortality and management costs.

Objectives:

To explore the potential of texture analysis based on run-length matrix features for classifying benign and malignant breast tumors in ultrasound imaging.

Methods:

A total of 70 breast tumors (38 benign and 32 malignant) have used in the proposed computer-aided diagnosis system. Twenty run-length matrix features have extracted for texture analysis in three normalizations (default, 3sigma, and 1% – 99%). Linear discriminant analysis and principal component analysis have employed to transform raw data to lower-dimensional spaces and increase discriminative power. The features have classified by the first nearest neighbor classifier.

Results:

The features under 3sigma normalization have designed via Linear discriminant analysis indicated high performance in classifying benign and malignant breast tumors with a sensitivity of 96.87%, specificity of 100%, accuracy of 98.57%, positive predictive value of 100%, and negative predictive value of 97.43%. The area under receiver operating characteristic curve was 0.992.

Conclusions:

Run-length matrix features had a high potential to characterize and could help radiologist to diagnosis breast tumors.

Sample Distributions After Two Texture Analysis Methods. A PCA; B LDA. MEF: Most Expressive Features; MDF: Most Discriminating Features; “1” and “2” Represented
Benign and Malignant Breast Tumors Respectively

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Ardakani AA

He received his Ph.D. in Medical Physics in 2018 from the Iran University of Medical Sciences (IUMS), specializing in medical imaging and using artificial intelligence in radiological diagnosis. His research interests focus on the physics of medical imaging systems, quantitative analysis of medical images, and applying artificial intelligence in diagnostic radiology procedures. He is an assistant professor of Medical Physics at Shahid Beheshti University of Medical Sciences.