Title :

Segmentation and classification of benign and malignant breast tumors via texture characterization from ultrasound images

Author(s) :

Benaouali, Mohamed [Auteur]
Bentoumi, Mohamed [Auteur]
Touati, Menad [Auteur]
Taleb-Ahmed, Abdelmalik [Auteur]
Mimi, Malika [Auteur]

Conference title :

2022 7th International Conference on Image and Signal Processing and their Applications (ISPA)

City :

Mostaganem

Country :

Algérie

Start date of the conference :

2022-05-08

Book title :

2022 7th International Conference on Image and Signal Processing and their Applications (ISPA)

Journal title :

Segmentation and classification of benign and malignant breast tumors via texture characterization from ultrasound images

Publisher :

IEEE

Publication date :

2022-06-03

English keyword(s) :

Ultrasound images
Breast tumor
Segmentation
HOG features
LBP features
Classification

HAL domain(s) :

Sciences de l'ingénieur [physics]

English abstract : [en]

The present paper deals with breast tumors classification from ultrasound images. The proposed procedure consists of four steps, namely preprocessing, segmentation, feature extraction and classification. To improve the ...
Show more >The present paper deals with breast tumors classification from ultrasound images. The proposed procedure consists of four steps, namely preprocessing, segmentation, feature extraction and classification. To improve the quality of ultrasound images, the preprocessing step consists of anisotropic filtering and histogram equalization that are performed on the original images. The segmentation is performed on the preprocessed images using the Level Set method that allows to extract the region of interest (ROI) and to reduce its size at the same time. Two feature extraction methods are used in this work namely, the local binary pattern (LBP) method and the histogram of oriented gradients (HOG) method. The two methods (LBP and HOG) are techniques of textures analysis and allow to characterize the ROI. The extracted feature sets constitute the inputs for three classifiers namely, support vector machines (SVM), k-nearest neighbors (KNN) and decision trees (DT). In this work, the best results are obtained by the concatenation of the two feature vectors namely LBP and HOG associated to the SVM classifier. This allows to achieve an accuracy of 96%, a sensitivity of 97% and a specificity of 94%.Show less >

Language :

Anglais

Peer reviewed article :

Oui

Audience :

Internationale

Popular science :

Non

Collections :

• Institut d'Électronique, de Microélectronique et de Nanotechnologie (IEMN) - UMR 8520

Source :

Harvested from HAL