Classification of breast ultrasound images based on texture analysis

Ultrasonography (USG) is a popular imaging modality because of its flexibility, non-invasion, non-ionisation and low cost. A breast ultrasound used to detect and classify abnormalities of the breast mass. However, the diagnosis is very subjective because it depends on the ability of the radiologist....

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Bibliographic Details
Published in2016 1st International Conference on Biomedical Engineering (IBIOMED) pp. 1 - 6
Main Authors Rahmawaty, Made, Nugroho, Hanung Adi, Triyani, Yuli, Ardiyanto, Igi, Soesanti, Indah
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.10.2016
Subjects
Breast
FBM
Feature extraction
Fractals
GLCM
Gray-scale
histogram equalization
Histograms
Image segmentation
MLP
morphological operation
region growing active contour
Ultrasonic imaging
ultrasound
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Summary:Ultrasonography (USG) is a popular imaging modality because of its flexibility, non-invasion, non-ionisation and low cost. A breast ultrasound used to detect and classify abnormalities of the breast mass. However, the diagnosis is very subjective because it depends on the ability of the radiologist. In order to eliminate operator dependency and to improve the diagnostic accuracy, a computerised system is necessary to do the feature extraction and the classification of the breast nodule. This research proposes a classification of breast USG images by using some texture features into two classes. The dataset consists of 57 USG images which grouped into 27 anechoic cases and 30 hypoechoic cases. An initial step of image pre-processing is conducted to enhance the detection capability. Afterwards, followed by some methods of morphological operation, region growing active contour and histogram equalization. The feature extraction method used texture analysis, which is histogram, gray level co-occurrence matrix (GLCM) and fractal Brownian motion (FBM). Finally, Multilayer Perceptron (MLP) classification method is used to classify anechoic nodule from hypoechoic nodule. The result shows that the proposed method achieved the accuracy of 91.23%, sensitivity of 95.83%, specificity of 87.88%, Positive Predictive Value (PPV) of 85.19% and Negative Predictive Value (NPV) of 96.67%. This suggest that the proposed method is excellent in analyzing breast USG images.
DOI:10.1109/IBIOMED.2016.7869825