Automatic suspicions lesions segmentation based on variable-size windows in mammography images
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ORIGINAL PAPER
Automatic suspicions lesions segmentation based on variable‑size windows in mammography images Bahram Sadeghi1 · Meysam Karimi1 · Samaneh Mazaheri2 Received: 11 May 2020 / Accepted: 29 October 2020 © IUPESM and Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract Breast cancer is the second main cause of death in women of western countries, so early detection and prevention is crucial. Early detection increases the likelihood of treatment as well as patient resistance. Among breast cancer detection methods, mammography is the most effective diagnostic method. For radiologists, diagnosing a cancerous mass on mammographic images is prone to error, which shows there is a need for a method to reduce the errors. In this study, a new adaptive thresholding method is proposed based on variable-sized windows. This method estimates the location of the mass and then determines the exact location of the cancerous tissue to reduce false positives. To detect the mass automatically, firstly, the histograms diagram and its relative maximums have been used to calculate the initial threshold for estimating the mass location. Two windows that contain information around each pixel and their size varies according to the mean value of each image due to the preservation of useful information. Secondly, two windows are used for the final threshold in order to discover the location of the mass and its exact shape. The proposed approach has been applied to 170 images of the Mammographic Image Analysis Society MiniMammographic database. Evaluations have shown 96.7% sensitivity and 0.79 false-positive rates, which prove an improvement in comparison with other state-of-the-art methods. Keywords Texture · Mammography · Segmentation · Computer-aided detection systems · Breast cancer · Adaptive thresholding
1 Introduction Breast cancer is the most common form of cancer among women. Tumors of breast cancer are small at first and sometimes it takes several years to become a large gland, So early detection of tissue is ciritial. Breast cancer detection methods include: Breast self-examination, Breast examination by a doctor and Mammography, which the most effective way to detect breast cancer is Mammography. Incorrect detections * Samaneh Mazaheri [email protected] Bahram Sadeghi [email protected] Meysam Karimi [email protected] 1
Department of Computer Science and Technology, Institute for Advanced Studies in Basic Sciences (IASBS), Zanjan, Iran
Faculty of Business and Information Technology, Ontario Tech University, Oshawa, ON, Canada
2
in mammograpgy images by radiologists errors of analysing diagnosis due to physician fatigue or optical illusion, lead to the idea of finding a solution to do the process automatically. Radiologists fail to detect tissue in 10 to 30 percent of cases. In order to reduce these human mistakes, computeraided detection/diagnosis (CAD) systems have been used [8]. Computer-aided detection systems are the most effective tools for early diagnosis of breas
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