Local Binary Patterns Descriptor Based on Sparse Curvelet Coefficients for False-Positive Reduction in Mammograms
المؤلفون المشاركون
Pawar, Meenakshi M.
Talbar, Sanjay N.
Dudhane, Akshay
المصدر
Journal of Healthcare Engineering
العدد
المجلد 2018، العدد 2018 (31 ديسمبر/كانون الأول 2018)، ص ص. 1-16، 16ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2018-09-25
دولة النشر
مصر
عدد الصفحات
16
التخصصات الرئيسية
الملخص EN
Breast Cancer is the most prevalent cancer among women across the globe.
Automatic detection of breast cancer using Computer Aided Diagnosis (CAD) system suffers from false positives (FPs).
Thus, reduction of FP is one of the challenging tasks to improve the performance of the diagnosis systems.
In the present work, new FP reduction technique has been proposed for breast cancer diagnosis.
It is based on appropriate integration of preprocessing, Self-organizing map (SOM) clustering, region of interest (ROI) extraction, and FP reduction.
In preprocessing, contrast enhancement of mammograms has been achieved using Local Entropy Maximization algorithm.
The unsupervised SOM clusters an image into number of segments to identify the cancerous region and extracts tumor regions (i.e., ROIs).
However, it also detects some FPs which affects the efficiency of the algorithm.
Therefore, to reduce the FPs, the output of the SOM is given to the FP reduction step which is aimed to classify the extracted ROIs into normal and abnormal class.
FP reduction consists of feature mining from the ROIs using proposed local sparse curvelet coefficients followed by classification using artificial neural network (ANN).
The performance of proposed algorithm has been validated using the local datasets as TMCH (Tata Memorial Cancer Hospital) and publicly available MIAS (Suckling et al., 1994) and DDSM (Heath et al., 2000) database.
The proposed technique results in reduction of FPs from 0.85 to 0.02 FP/image for MIAS, 4.81 to 0.16 FP/image for DDSM, and 2.32 to 0.05 FP/image for TMCH reflecting huge improvement in classification of mammograms.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Pawar, Meenakshi M.& Talbar, Sanjay N.& Dudhane, Akshay. 2018. Local Binary Patterns Descriptor Based on Sparse Curvelet Coefficients for False-Positive Reduction in Mammograms. Journal of Healthcare Engineering،Vol. 2018, no. 2018, pp.1-16.
https://search.emarefa.net/detail/BIM-1187442
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Pawar, Meenakshi M.…[et al.]. Local Binary Patterns Descriptor Based on Sparse Curvelet Coefficients for False-Positive Reduction in Mammograms. Journal of Healthcare Engineering No. 2018 (2018), pp.1-16.
https://search.emarefa.net/detail/BIM-1187442
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Pawar, Meenakshi M.& Talbar, Sanjay N.& Dudhane, Akshay. Local Binary Patterns Descriptor Based on Sparse Curvelet Coefficients for False-Positive Reduction in Mammograms. Journal of Healthcare Engineering. 2018. Vol. 2018, no. 2018, pp.1-16.
https://search.emarefa.net/detail/BIM-1187442
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1187442
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر