title: Optimization of breast lesion segmentation in texture feature space approach.
abstract: This paper develops a method for semi-automatic detection of breast lesion boundaries by combining the snake evolution techniques with statistical texture information of images. We propose an efficient image energy function in segmentation based on image features, first-order textural features and four n×n masks. The segmentation results were evaluated by using area error rate. The image features were evaluated qualitatively by using the contrast-to-noise ratio and fractal dimension analysis. In our study, standard deviation, skewness and entropy are indicated as being the most relevant image features.
authors.affiliation: Department of Chemistry, Physics and Environment, Dunarea de Jos University of Galati, 111 Domneasca Street, 800201 Galati, Romania. Electronic address: Luminita.Moraru@ugal.ro.
authors.forename: Luminita
authors.initials: L
authors.lastname: Moraru
authors.affiliation: Department of Chemistry, Physics and Environment, Dunarea de Jos University of Galati, 111 Domneasca Street, 800201 Galati, Romania.
authors.forename: Simona
authors.initials: S
authors.lastname: Moldovanu
authors.affiliation: Department of Mathematical Sciences, Delaware State University, Dover, DE 19901-2277, USA; Department of Mathematics, Faculty of Science, King Abdulaziz University, Jeddah, Saudi Arabia.
authors.forename: Anjan
authors.initials: A
authors.lastname: Biswas
mesh_terms: D000465:Algorithms; D001941:Breast Diseases / Q000000981:diagnostic imaging*; D001943:Breast Neoplasms / Q000000981:diagnostic imaging; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D059629:Signal-To-Noise Ratio; D016217:Ultrasonography, Mammary / Q000379:methods*
keywords: Active contours models; Area error rate; Image feature; Ultrasound images