Title :
Comparison of feature extraction algorithms for mammography images
Author :
Kitanovski, Ivan ; Jankulovski, Blagojce ; Dimitrovski, Ivica ; Loskovska, Suzana
Author_Institution :
Fac. of Electr. Eng. & Inf. Technol., Dept. of Comput. Sci. & Eng., Ss. "Cyril and Methodius" Univ., Skopje, Macedonia
Abstract :
Mammography image classification is a very important research field due to its domain of implementation. The aim of this paper is to compare feature extraction methods and to test them on a variety of classifiers. Five feature extraction methods were used: LBP, GLDM, GLRLM, Haralick and Gabor texture features. Three classification algorithms were used during the experiments, namely, support vector machines, k-nearest neighbor and c4.5 algorithm. The experiments were conducted on the MIAS database. The results show that GLDM is the most appropriate feature extraction method for images from this database.
Keywords :
feature extraction; image texture; mammography; medical image processing; support vector machines; GLDM texture features; GLRLM texture features; Gabor texture features; Haralick texture features; LBP texture features; MIAS database; SVM; c4.5 algorithm; feature extraction algorithms; k-nearest neighbor; mammography image classification; support vector machines; Accuracy; Breast cancer; Classification algorithms; Databases; Feature extraction; Support vector machines; c4.5; gabor; gldm; glrlm; haralick; image classification; k-nn; lbp; mamography; svm;
Conference_Titel :
Image and Signal Processing (CISP), 2011 4th International Congress on
Conference_Location :
Shanghai
Print_ISBN :
978-1-4244-9304-3
DOI :
10.1109/CISP.2011.6100285
