Corpus ID: 7406049

Breast Cancer Detection Using Multilevel Thresholding

  title={Breast Cancer Detection Using Multilevel Thresholding},
  author={Y. Ireaneus Anna Rejani and S. Thamarai Selvi},
This paper presents an algorithm which aims to assist the radiologist in identifying breast cancer at its earlier stages. It combines several image processing techniques like image negative, thresholding and segmentation techniques for detection of tumor in mammograms. The algorithm is verified by using mammograms from Mammographic Image Analysis Society. The results obtained by applying these techniques are described. 
Digital Mammography: A Review on Detection of Breast Cancer
This paper is a survey of Digital image processing techniques that play a vital role in assisting the biopsies of mammogram images and the results are studied and analyzed. Expand
Digital Mammogram Segmentation and Feature Extraction: A Review
Breast cancer has become a prominent threat, leading to woman mortality. Early detection is the best solution to combat this mortality. Digital mammography is the most reliable technique forExpand
Performance evaluation of breast lesion detection systems with expert delineations: a comparative investigation on mammographic images
Evaluating the performance of six popular breast tumor detection techniques with manual delineations provided by two experienced radiologists on the mammographic images concluded that computer-aided lesion detection systems can be used to assist Radiologists in routine clinical practice for the detection of breast tumors in mammography images. Expand
Breast cancer in females is the most common cancer diseases and leading cause of death. In the recent years, Computer Aided Diagnosis (CAD) is very useful for detection of breast cancer. MammographyExpand
An enhancement of mammogram images for breast cancer classification using artificial neural networks
In this proposed method a novel hybrid optimum feature selection (HOFS) method is used to find out the significant features to reach maximum accuracy for this classification of mammogram images. Expand
Prognosis and Diagnosis of Breast Cancer Using Interactive Dashboard Through Big Data Analytics
Background: Cancer is a life threatening disease of present scenario among which breast cancer is the second highly mortal disease in women. There are several stages of cancer and an early detectionExpand
ii I hereby declare that all information in this document has been obtained and presented in accordance with academic rules and ethical conduct. I also declare that, as required by these rules andExpand
A New Method for Segmentation using Fractal Properties of Images
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The algorithm was notably successful in the detection of minimal cancers manifested by masses, and an extensive study of the effects of the algorithm's parameters on its sensitivity and specificity was performed in order to optimize the method for a clinical, observer performance study. Expand
An artificial intelligent algorithm for tumor detection in screening mammogram
  • Lei Zhen, A. Chan
  • Mathematics, Medicine
  • IEEE Transactions on Medical Imaging
  • 2001
An algorithm that combines several artificial intelligent techniques with the discrete wavelet transform (DWT) for detection of masses in mammograms and a tree-type classification strategy is applied at the end to determine whether a given region is suspicious for cancer. Expand
Adaptive CAD modules for mass detection in digital mammography
  • W. Qian, Lihua Li, L. Clarke, F. Mao, R. Clark
  • Computer Science
  • Proceedings of the 20th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Vol.20 Biomedical Engineering Towards the Year 2000 and Beyond (Cat. No.98CH36286)
  • 1998
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A segmentation algorithm that is fully automated and can operate independent of type of digitizing system, image orientation, and image projection and can serve as a component of an "intelligent" workstation for computer-aided diagnosis in mammography. Expand
Sites of Occurrence of Malignancies in Mammograms
It has been observed clinically that breast cancers occur most frequently in the upper outer quadrant of the breast, and that cancers are more often associated with glandular than with fatty tissue. Expand
On Digital Mammogram Segmentation and Microcalcification Detection Using Multiresolution Wavelet Analysis
A multiresolution wavelet analysis (MWA) and nonstationary Gaussian Markov random field (GMRF) technique is introduced for the detection of microcalcifications with high accuracy and the approach has been tested with a number of mammographic images. Expand
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Wavelet-based image denoising using a Markov random field a priori model
A comparison of quantitative and qualitative results for test images demonstrates the improved noise suppression performance with respect to previous wavelet-based image denoising methods. Expand