Automatic detection and segmentation of microcalcifications on digitized mammograms

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 (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 (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.

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Automatic detection and segmentation of microcalcifications on digitized mammograms

Cited by 4 publications

References 1 publication

Automatic Segmentation of Micro-calcification Based on SIFT in Mammograms

Automatic Segmentation of Micro-calcification Based on SIFT in Mammograms

Artificial neural classification of clustered microcalcifications on digitized mammograms

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

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