Detection of partially overlapped masses in mammograms

Abstract

<span>Breast cancer remains one of the major causes of cancer deaths among women. For decades, screening mammography has been one of the most common methods for early cancer detection and diagnosis. Digital mammography images are created by applying a small burst of x-rays that pass through the breast to a solid-state detector, which transmits the electronic signals to a computer to form a digital image. However, due to projection, some mass areas may be partially covered, which makes them difficult to be interprated. This paper addresses the issue of potential mass regions being distorted by other normal breast tissues, which will negatively affect some of the features being extracted from the mass and in turn deteriorate the classification accuracy. The goal was to estimate the overlapped parts of the mass border using Euclidean distance in order to give more accurate results in next stages. The presented method achieved 95.744% region sensitivity at 0.333 False Positive per Image (FPI), outperforming other researches in this branch of mammography analysis.</span>

Automated breast cancer detection system from breast mammogram using deep neural network

“…These two operators produce low decision values which help the exploitation phase to choose the best ideal dataset. The exploitation phase is mathematically modelled as (8).…”

“…Mammography is one of the commonly used and believed methods to identify the lumps in the breast. The mammography images show the presence of cancerous lumps/ microcalcifications in the breast [8]. The cancerous lumps in the mammography images are tiny in size and its image contrast is low.…”

Early detection of slight bruises in apples by cost-efficient near-infrared imaging

“…From the ROI image and the apple mask, the apple-masked ROI image was created. Contrast limited adaptive histogram equalization (CLAHE), which could enhance the image areas with low contrast [43]- [45] was then applied to the apple-masked ROI image. The resulting image is the so-called CLAHE image in which the contrast of the bruise region was enhanced.…”