title: Segmentation and detection of breast cancer in mammograms combining wavelet analysis and genetic algorithm.
abstract: In Brazil, the National Cancer Institute (INCA) reports more than 50,000 new cases of the disease, with risk of 51 cases per 100,000 women. Radiographic images obtained from mammography equipments are one of the most frequently used techniques for helping in early diagnosis. Due to factors related to cost and professional experience, in the last two decades computer systems to support detection (Computer-Aided Detection - CADe) and diagnosis (Computer-Aided Diagnosis - CADx) have been developed in order to assist experts in detection of abnormalities in their initial stages. Despite the large number of researches on CADe and CADx systems, there is still a need for improved computerized methods. Nowadays, there is a growing concern with the sensitivity and reliability of abnormalities diagnosis in both views of breast mammographic images, namely cranio-caudal (CC) and medio-lateral oblique (MLO). This paper presents a set of computational tools to aid segmentation and detection of mammograms that contained mass or masses in CC and MLO views. An artifact removal algorithm is first implemented followed by an image denoising and gray-level enhancement method based on wavelet transform and Wiener filter. Finally, a method for detection and segmentation of masses using multiple thresholding, wavelet transform and genetic algorithm is employed in mammograms which were randomly selected from the Digital Database for Screening Mammography (DDSM). The developed computer method was quantitatively evaluated using the area overlap metric (AOM). The mean ± standard deviation value of AOM for the proposed method was 79.2 ± 8%. The experiments demonstrate that the proposed method has a strong potential to be used as the basis for mammogram mass segmentation in CC and MLO views. Another important aspect is that the method overcomes the limitation of analyzing only CC and MLO views.
authors: Centro de Matemática, Computação e Cognição, Universidade Federal do ABC, Rua Santa Adélia, 166, 09210170 Santo André, SP, Brazil. Electronic address: danilo.pereira@ufabc.edu.br.
authors: Danilo Cesar
authors: DC
authors: Pereira
authors: Colegiado de Engenharia Elétrica, Universidade Federal do Vale do São Francisco, Avenida Antônio Carlos Magalhães, 510, 48902300 Juazeiro, BA, Brazil. Electronic address: rodrigo.ramos@univasf.edu.br.
authors: Rodrigo Pereira
authors: RP
authors: Ramos
authors: Faculty of Computation (FACOM), Federal University of Uberlândia (UFU), Av. João Naves de Ávila, 2121, 38400902 Uberlândia, MG, Brazil. Electronic address: marcelo.zanchetta@gmail.com.
authors: Marcelo Zanchetta
authors: MZ
authors: do Nascimento
mesh_terms: D000465:Algorithms; D016477:Artifacts; D001943:Breast Neoplasms / Q000000981:diagnostic imaging*; D005260:Female; D006801:Humans; D008327:Mammography
keywords: Computer-aided systems; Genetic algorithm; Image segmentation; Mammographic images; Wavelet transform