Texture-based analysis of clustered microcalcifications detected on mammograms
Section snippets
Alain Tiedeu received his doctorate degree from the University of Yaoundé I, Cameroon, in 1995. He has been teaching electronics, digital signal processing, artificial neural networks, digital image processing, and related subjects at the National Advanced School of Engineering for the past decade. Professor Tiedeu has also served as reviewer, program committee member, and on editorial advisory board of a number of international conferences and journals (IEEE SITIS conference series, WSEAS
References (43)
- et al.
A CAD system for the 3D location of lesions in mammograms
Medical Image Anal.
(2002) - et al.
Correspondences between microcalcification projections on two mammographic views acquired with digital systems
Comput. Medical Imaging Graphics
(2005) - et al.
Detection of microcalcifications in digital mammograms using wavelet filter and Markov random field model
Comput. Medical Imaging Graphics
(2006) - et al.
Detection of microcalcifications in digital mammograms using combined model-based and statistical textural features
Expert Systems Appl.
(2010) Computer-aided detection to improve screening mammography
Breast Imaging
(1989)- et al.
Teaching Atlas of Mammography
(1985) - et al.
The current detectability of breast cancer in a mammographic screening program
Cancer
(1993) - et al.
Region-based contrast enhancement of mammograms
IEEE Trans. Medical Imaging
(1992) - et al.
Adaptive mammographic image enhancement using first derivative and local statistics
IEEE Trans. Medical Imaging
(1997)
Mammographic feature enhancement by multiscale analysis
IEEE Trans. Medical Imaging
Fractal modelling and segmentation for the enhancement of microcalcifications in digital mammograms
IEEE Trans. Medical Imaging
A novel approach to microcalcification detection using fuzzy logic technique
IEEE Trans. Medical Imaging
A method for detecting microcalcifications in digital mammograms
J. Digital Imaging
Multiscale contrast enhancement for radiographies: Laplacian pyramid versus fast wavelet transform
IEEE Trans. Medical Imaging
Normalization of local contrast in mammograms
IEEE Trans. Medical Imaging
A CAD system for the automatic detection of clustered microcalcifications in digitized mammograms films
IEEE Trans. Medical Imaging
Automatic detection of clustered microcalcifications in digitized mammograms films
J. Electronic Imaging
Cited by (34)
Multiscale connected chain topological modelling for microcalcification classification
2019, Computers in Biology and MedicineCitation Excerpt :These are classified as malignant based on certain characteristics like size, shape, form, number, density, distribution pattern and cluster pattern [2]. Breast microcalcifications are small spots of calcium deposits which are represented as white specks in mammograms [4,5] as shown in Fig. 1. Though most detected microcalcifications are benign, the presence of fine and granular patterned microcalcifications could be an early indication of breast carcinoma requiring further investigation and potentially treatment [4].
Hybrid methods for feature extraction for breast masses classification
2018, Egyptian Informatics JournalCitation Excerpt :To improve the efficiency and precision of a CAD system, it is important to extract the most discriminative features in an efficient way. Texture features have been commonly used in the analysis and interpretation of mammogram images [7,8]. Texture features based approaches use Gabor filters [9], Local Binary Pattern (LBP) [10,11], and Discreet Cosine Transform (DCT) to encode texture information of mammogram images [12].
Mammogram classification using sparse-ROI: A novel representation to arbitrary shaped masses
2016, Expert Systems with ApplicationsCitation Excerpt :As the earlier works in statistical matrices applicable to rectangular shape image only, two new algorithms are developed namely GLCM_Sparse-ROI and GLAM_Sparse-ROI. The features extracted from these are listed as (Tiedeu, Daul, Kentsop, Graebling, & Wolf, 2012; Mohanty, Senapati, & Lenka, 2013c): (1) ASM Homogenity; (2) Contrast; (3) Local homogenity; (4) Correlation; (5) Dissimilarity; (6) Entropy; (7) Sum of squares; (8) Interia; (9) Cluster shade; (10) Cluster prominence (11) Difference Entropy (12) Sum Entropy; (13) Sum average. As the SVM is the popular classification method, a Matlab function MultiSVM is used to classify the mammograms.
A new method of detecting micro-calcification clusters in mammograms using contourlet transform and non-linking simplified PCNN
2016, Computer Methods and Programs in BiomedicineCitation Excerpt :A variety of techniques have been used in different steps. For mammogram enhancement, variety attempts have been done, such as improved histogram equalization [4], image enhancement based on wavelet fusion [5], automated lesion intensity enhance [6], modified multifractal analysis [7], etc.; in the segmentation step, many techniques have been suggested, such as multistable cellular neural networks, geodesic active contours (GAC) technique associated with anisotropic texture filtering [8], case-adaptive decision rule method [9], new scale-specific blob detection technique [10], etc.; in the third step, select true MCs by extracting a group of features of micro-calcifications like moment-based geometrical features [11], wavelet feature and Gabor feature [12] and so on. These aforementioned techniques make great contributions, however because the MCs detection faces different difficulties, the hybrid detection algorithms combining different theories seems more popular.
A robust algorithm for white blood cell nuclei segmentation
2022, Multimedia Tools and Applications
Alain Tiedeu received his doctorate degree from the University of Yaoundé I, Cameroon, in 1995. He has been teaching electronics, digital signal processing, artificial neural networks, digital image processing, and related subjects at the National Advanced School of Engineering for the past decade. Professor Tiedeu has also served as reviewer, program committee member, and on editorial advisory board of a number of international conferences and journals (IEEE SITIS conference series, WSEAS conference series, RPBME, etc.). A former regular associate member of the Abdus Salam International Centre for Theoretical Physics, his research interests include biomedical instrumentation and modelling, and medical signal and image processing and analysis.
Christian Daul received the Ph.D. degree in computer vision from the Université Louis Pasteur (ULP), Strasbourg, France, in 1994. From 1990 to 1995, he was with the Laboratoire des Sciences de lʼImage, de lʼInformatique et dela Télédétection (LSIIT UMR 7005 CNRS/UdS) before joining the Institut of Industrial Mathematics (ITWM, Fraunhofer Institut), Kaiserslautern, Germany, where he was a member of the Image Processing Group. Since October 1999, he has been with the Centre de Recherche en Automatique de Nancy (CRAN UMR 7039 CNRS/Nancy University), Nancy, France, where he is currently working in the area of medical imaging (mammography, endoscopy, radiotherapy, and cardiology). His main research interests include image segmentation, data registration, and 3D data reconstruction. He is University Professor at the Institut National Polytechnique de Lorraine (EEIGM/INPL), Vandœuvre-Les-Nancy, France, where he is teaching in the signal processing field.
Aude Kentsop is a holder of a Master degree in Computer Science from the University of Yaoundé I since 2004. She is currently on a Ph.D. program at the “Ecole de Technologie Supérieure”, Canada.
Pierre Graebling received the Ph.D. degree in Image Processing from the Université Louis Pasteur (ULP), Strasbourg, France, in 1992. He was a Professor in computer science of the University of Strasbourg, Illkirch, France, where he was a member of the EAVR team of the Laboratoire des Sciences de lʼImage, de lʼInformatique et de la Télédétection (LSIIT). His research interests have included image processing, computer vision and medical imaging. In addition to the above topics, his teaching interests were programming, networks, and operating systems.
Didier Wolf received the Ph.D. degree in Electrical Engineering from the Institut National Polytechnique de Lorraine, Nancy, France, in 1986. Currently, he is University Professor at the Institut National Polytechnique de Lorraine, where he is teaching in the signal processing field. Since 2005, he has been the Deputy Director of the Centre de Recherche en Automatique de Nancy (CRAN UMR 7039 CNRS/Nancy University), where he is the head of the biomedical engineering team. His main research interests include image processing, signal processing, and medical imaging techniques applied in the fields of cancerology and cardiology.
- 1
Professor Graebling passed away in February 2011 from a cancer. The authors dedicate this paper to him. It was a great pleasure working with him as a colleague and scientist. Those who were close to him enjoyed his capacity for dialog, his deep sense of humanity and humor and his courage in the face of trials.