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A Hybrid Image Filtering Method for Computer-Aided Detection of Microcalcification Clusters in Mammograms  [PDF]
Xiaoyong Zhang,Noriyasu Homma,Shotaro Goto,Yosuke Kawasumi,Tadashi Ishibashi,Makoto Abe,Norihiro Sugita,Makoto Yoshizawa
Journal of Medical Engineering , 2013, DOI: 10.1155/2013/615254
Abstract: The presence of microcalcification clusters (MCs) in mammogram is a major indicator of breast cancer. Detection of an MC is one of the key issues for breast cancer control. In this paper, we present a highly accurate method based on a morphological image processing and wavelet transform technique to detect the MCs in mammograms. The microcalcifications are firstly enhanced by using multistructure elements morphological processing. Then, the candidates of microcalcifications are refined by a multilevel wavelet reconstruction approach. Finally, MCs are detected based on their distributions feature. Experiments are performed on 138 clinical mammograms. The proposed method is capable of detecting 92.9% of true microcalcification clusters with an average of 0.08 false microcalcification clusters detected per image. 1. Introduction Breast cancer is one of the major causes of mortality in middle-aged women, especially in developed countries [1]. At present, there are no effective ways to prevent breast cancer since its cause remains unknown [2]. Therefore, early detection becomes the key to improving the breast cancer prognosis and reducing the mortality rates. Mammography has been widely recognized as being one of the most effective imaging modalities for early detection of breast cancer. However, it is a hard work for radiologists to provide both accurate and uniform evaluation for the enormous number of mammograms generated in widespread screening. A computer-aided detection or diagnosis (CAD) system, which uses computer technologies to detect the typical signs of breast cancer, has been developed to provide a “second opinion” for radiologists and to improve the accuracy and stability of diagnosis. In general, there are three signs of breast cancer in a mammogram: microcalcification clusters (MCs), architectural distortions, and masses [2]. In this paper, we particularly focus on the detection of MCs since they appear in 30–50% of mammographic diagnosed cases and show a high correlation with breast cancer [3]. According to the Breast Image Reporting and Data System (BI-RADS) lexicon [4], MCs are tiny calcium deposits that appear as small bright spots in mammograms. As an example, Figure 1 shows an MC in a mediolateral-oblique (MLO) mammogram. It is often hard for radiologists to find individual MCs in mammograms because they are very small (typically, 0.05–1?mm [3]) in the size and the contrast between the MCs and the surrounding breast tissue is not high enough. Figure 1: An example of an MC. (a) A mediolateral-oblique (MLO) mammogram. (b) Expanded view
A Computer Aided Detection system for mammographic images implemented on a GRID infrastructure  [PDF]
U. Bottigli,P. Cerello,P. Delogu,M. E. Fantacci,F. Fauci,G. Forni,B. Golosio,P. L. Indovina,A. Lauria,E. Lopez Torres,R. Magro,G. L. Masala,P. Oliva,R. Palmiero,G. Raso,A. Retico,A. Stefanini,S. Stumbo,S. Tangaro
Physics , 2003,
Abstract: The use of an automatic system for the analysis of mammographic images has proven to be very useful to radiologists in the investigation of breast cancer, especially in the framework of mammographic-screening programs. A breast neoplasia is often marked by the presence of microcalcification clusters and massive lesions in the mammogram: hence the need for tools able to recognize such lesions at an early stage. In the framework of the GPCALMA (GRID Platform for Computer Assisted Library for MAmmography) project, the co-working of italian physicists and radiologists built a large distributed database of digitized mammographic images (about 5500 images corresponding to 1650 patients) and developed a CAD (Computer Aided Detection) system, able to make an automatic search of massive lesions and microcalcification clusters. The CAD is implemented in the GPCALMA integrated station, which can be used also for digitization, as archive and to perform statistical analyses. Some GPCALMA integrated stations have already been implemented and are currently on clinical trial in some italian hospitals. The emerging GRID technology can been used to connect the GPCALMA integrated stations operating in different medical centers. The GRID approach will support an effective tele- and co-working between radiologists, cancer specialists and epidemiology experts by allowing remote image analysis and interactive online diagnosis.
Clustered Microcalcification Detection in Digital Mammograms Based on an Active Learning with Support Vector Machine
基于主动支持向量机的乳腺癌微钙化簇检测

FENG Jun,JIANG Jun,Ip Ho-Shing Horace,WANG Hui-ya,
冯筠
,姜军,叶豪盛,王惠亚

计算机科学 , 2010,
Abstract: Clustered microcalcification is an important signal for breast cancer in the early stages.However,computer aided detection of microcalcification is a challenge in the field of medical imaging.To improve the performance of the detection system,a large amount of lesion labeling is essential.Besides the difficulty on collecting samples itself,it also takes experts much time for manual labeling.Few state-of-the-art techniques take into account this problem W first applied the techniques of active learning with ...
A multi-scale approach to the computer-aided detection of microcalcification clusters in digital mammograms  [PDF]
P. Delogu,M. E. Fantacci,A. Retico,A. Stefanini,A. Tata
Physics , 2007,
Abstract: A computer-aided detection (CADe) system for the identification of microcalcification clusters in digital mammograms has been developed. It is mainly based on the application of wavelet transforms for image filtering and neural networks for both the feature extraction and the classification procedures. This CADe system is easily adaptable to different databases. We report and compare the FROC curves obtained on the private database we used for developing the CADe system and on the publicly available MIAS database. The results achieved on the two databases show the same trend, thus demonstrating the good generalization capability of the system.
Comparing Methods for segmentation of Microcalcification Clusters in Digitized Mammograms  [PDF]
Hajar Moradmand,Saeed Setayeshi,Hossein Khazaei Targhi
Computer Science , 2012,
Abstract: The appearance of microcalcifications in mammograms is one of the early signs of breast cancer. So, early detection of microcalcification clusters (MCCs) in mammograms can be helpful for cancer diagnosis and better treatment of breast cancer. In this paper a computer method has been proposed to support radiologists in detection MCCs in digital mammography. First, in order to facilitate and improve the detection step, mammogram images have been enhanced with wavelet transformation and morphology operation. Then for segmentation of suspicious MCCs, two methods have been investigated. The considered methods are: adaptive threshold and watershed segmentation. Finally, the detected MCCs areas in different algorithms will be compared to find out which segmentation method is more appropriate for extracting MCCs in mammograms.
Neuro-Fuzzy Approach to Microcalcification Contrast Enhancement in Digitized Mammogram Images
Ayman AbuBaker
International Journal of Multimedia & Its Applications , 2012,
Abstract: Computer aided diagnoses can assists radiologists in detecting microcalcification, crucial evidence in mammogram for the early diagnosis of breast cancer. A novel approach is proposed in this paper for early detection of breast cancer by enhancing microcalcification regions in mammogram images using hybrid neuro-fuzzy technique. As a first stage, the mammogram intensities are fuzzified using three linguistic labels. Then, the inference engine of a classical fuzzy system is replaced by a collection of sixteen parallel neural networks and a cascade neural network in order to reduce the computational time for real-time applications. The parallel cascade neural networks are trained using data sets that randomly selected from the original fuzzy decision matrix. Finally, the value of the local mask centre is enhanced after defuzzification the input sets. This work is extensively evaluated using two different types of resources which are Mammographic Image Analysis Society database (MIAS) and University of South Florida (USF) database. As a result, it found to be sensitive in enhancing the microcalcifications regions in mammogram with very little number false positive regions.
Comparing Methods for segmentation of Microcalcification Clusters in Digitized Mammograms
Hajar Moradmand,Saeed Setayeshi,Hossein Khazaei Targhi
International Journal of Computer Science Issues , 2011,
Abstract: The appearance of microcalcifications in mammograms is one of the early signs of breast cancer. So, early detection of microcalcification clusters (MCCs) in mammograms can be helpful for cancer diagnosis and better treatment of breast cancer. In this paper a computer system devised to support a radiologist in detection MCCs in digital mammography has been proposed. First, to facilitate and improve detection step, the mammogram images have been enhanced with wavelet transformation and morphology operation. Then for segmentation of suspicious MCCs, two methods have been investigated. The considered methods are: adaptive threshold and Watershed segmentation. The purpose of this paper is to find out which segmentation method is more appropriate for extracting suspicious areas that contain MCCs in mammograms. Finally the MCCs detection areas in different algorithms will be compared.
A scalable Computer-Aided Detection system for microcalcification cluster identification in a pan-European distributed database of mammograms  [PDF]
A. Retico,P. Delogu,M. E. Fantacci,A. Preite Martinez,A. Stefanini,A. Tata
Physics , 2007, DOI: 10.1016/j.nima.2006.08.094
Abstract: A computer-aided detection (CADe) system for microcalcification cluster identification in mammograms has been developed in the framework of the EU-founded MammoGrid project. The CADe software is mainly based on wavelet transforms and artificial neural networks. It is able to identify microcalcifications in different kinds of mammograms (i.e. acquired with different machines and settings, digitized with different pitch and bit depth or direct digital ones). The CADe can be remotely run from GRID-connected acquisition and annotation stations, supporting clinicians from geographically distant locations in the interpretation of mammographic data. We report the FROC analyses of the CADe system performances on three different dataset of mammograms, i.e. images of the CALMA INFN-founded database collected in the Italian National screening program, the MIAS database and the so-far collected MammoGrid images. The sensitivity values of 88% at a rate of 2.15 false positive findings per image (FP/im), 88% with 2.18 FP/im and 87% with 5.7 FP/im have been obtained on the CALMA, MIAS and MammoGrid database respectively.
A New Ensemble Learning Approach for Microcalcification Clusters Detection  [cached]
Xinsheng Zhang
Journal of Software , 2009, DOI: 10.4304/jsw.4.9.1014-1021
Abstract: A new microcalcification clusters (MCs) detection method in mammograms is proposed, which is based on a new ensemble learning method. In this paper, , we propose a bagging with adaptive cost adjustment ensemble algorithm; and a new ensemble strategy, called boosting with relevance feedback, by embedding the relevance feedback technique into the heterogenous base learner training, and meanwhile carefully design an effectively systematical feedback scheme, which promise the preventing of overfitting. The ground truth of MCs is assumed to be known as a priori. In our algorithm, each MCs is enhanced by a well designed high-pass filter. Then the 116 dimentional image features are extracted by the feature extractor and fed to the ensemble decision model. In image feature domain, the MCs detection procedure is formulated as a supervised learning and classification problem, and the trained ensemble model is used as a classifier to decide the presence of MCs or not. Case study on microcalcification clusters detection for breast cancer diagnosis illustrates that the proposed algorithm is not only effective but also efficient.
A Novel Method for the Detection of Microcalcifications Based on Multi-scale Morphological Gradient Watershed Segmentation Algorithm
S. Vijaya Kumar,M.Naveen Lazarus,,C. Nagaraju
International Journal of Engineering Science and Technology , 2010,
Abstract: This paper presents an automated system for detecting masses in mammogram images. Breast cancer is one of the leading causes of women mortality in the world. Since the causes are unknown, breast cancer cannot be prevented. It is difficult for radiologists to provide both accurate and uniform evaluation over the enormous number of ammograms generated in widespread screening. Microcalcifications (calcium deposits) and masses are the earliest signs of breast carcinomas and their detection is one of the key issues for breast cancer control. Computer-aided detection of Microcalcifications and masses is an important and challenging task in breast cancer control. This paper presents a novel approach for detecting microcalcification clusters. First digitized mammogram has been taken from Mammography Image Analysis Society (MIAS) database. The Mammogram is preprocessed using Adaptive median filtered. Next, the microcalcification clusters are identified by using the marker extractions of the gradient images obtained by multiscale morphological reconstruction and avoids Oversegmentation vivid in Watershed algorithm. Experimental result show that the microcalcification can be accurately and efficiently detected using the proposed approach.
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