Comput Methods Programs Biomed - Segmentation and detection of breast cancer in mammograms combining wavelet analysis and genetic algorithm.


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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.

Resumo Limpo

brazil nation cancer institut inca report new case diseas risk case per women radiograph imag obtain mammographi equip one frequent use techniqu help earli diagnosi due factor relat cost profession experi last two decad comput system support detect computeraid detect cade diagnosi computeraid diagnosi cadx develop order assist expert detect abnorm initi stage despit larg number research cade cadx system still need improv computer method nowaday grow concern sensit reliabl abnorm diagnosi view breast mammograph imag name craniocaud cc mediolater obliqu mlo paper present set comput tool aid segment detect mammogram contain mass mass cc mlo view artifact remov algorithm first implement follow imag denois graylevel enhanc method base wavelet transform wiener filter final method detect segment mass use multipl threshold wavelet transform genet algorithm employ mammogram random select digit databas screen mammographi ddsm develop comput method quantit evalu use area overlap metric aom mean standard deviat valu aom propos method experi demonstr propos method strong potenti use basi mammogram mass segment cc mlo view anoth import aspect method overcom limit analyz cc mlo view

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