Detection of microcalcification in digitized mammograms with multistable cellular neural networks using a new image enhancement method: automated lesion intensity enhancer (ALIE)

Authors : Levent CİVCİK, Burak YILMAZ, Yüksel ÖZBAY, Ganime Dilek EMLİK

Pages : 853-872

Doi:10.3906/elk-1303-139

View : 16 | Download : 5

Publication Date : 0000-00-00

Article Type : Research Paper

Abstract :Microcalcification detection is a very important issue in early diagnosis of breast cancer. Generally physicians use mammogram images for this task; however, sometimes analyzing these images become a hard task because of problems in images such as high brightness values, dense tissues, noise, and insufficient contrast level. In this paper, we present a novel technique for the task of microcalcification detection. This technique consists of three steps. The first step is focused on removing pectoral muscle and unnecessary parts from the mammogram images by using cellular neural networks insert ignore into journalissuearticles values(CNNs);, which makes this a novel process. In the second step, we present a novel image enhancement technique focused on enhancing lesion intensities called the automated lesion intensity enhancer insert ignore into journalissuearticles values(ALIE);. In the third step, we use a special CNN structure, named multistable CNNs. After applying the combination of these methods on the MIAS database, we achieve 82.0% accuracy, 90.9% sensitivity, and 52.2% specificity values.
Keywords : Mammogram, microcalcification, cellular neural networks, image processing, image enhancement, automated lesion intensity enhancer, pectoral muscle