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Das J, Barman Mandal S. Identification of Homo sapiens cancer classes based on fusion of hidden gene features. J Biomed Inform 2020; 110:103555. [PMID: 32916304 DOI: 10.1016/j.jbi.2020.103555] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2020] [Revised: 07/08/2020] [Accepted: 09/02/2020] [Indexed: 10/23/2022]
Abstract
Classification of Homo sapiens cancer genes in molecular level is a challenging research issue as they are extremely pseudo random in nature. Signature gene features need to be exposed to distinctly identify the gene class. Tree-structured filter bank is chosen to perform feature extraction and dimension reduction of the genes. Extracted gene features are fused using Gaussian mixture probability distribution function and identify different cancer classes depending on amount of correlation and exploiting maximum likelihood function. The algorithm is tested on 161 sample gene data of 7 different cancer classes. Sensitivity, specificity, accuracy, precision and F-score are used as metrics to judge the performance of the system and ROC is plotted in comparison with existing electrical network model based classifier. The proposed classifier can identify more than stated number of cancer classes which is a major limitation of the existing electrical network based method. The proposed algorithm is validated by comparing the results with other seven existing image processing based methods.
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Affiliation(s)
- Joyshri Das
- Institute of Radio Physics & Electronics, University of Calcutta, India.
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Elter M, Horsch A. CADx of mammographic masses and clustered microcalcifications: A review. Med Phys 2009; 36:2052-68. [PMID: 19610294 DOI: 10.1118/1.3121511] [Citation(s) in RCA: 141] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Affiliation(s)
- Matthias Elter
- Fraunhofer Institute for Integrated Circuits, Am Wolfsmantel 33, 91058 Erlangen, Germany.
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Giger ML, Chan HP, Boone J. Anniversary paper: History and status of CAD and quantitative image analysis: the role of Medical Physics and AAPM. Med Phys 2009; 35:5799-820. [PMID: 19175137 PMCID: PMC2673617 DOI: 10.1118/1.3013555] [Citation(s) in RCA: 165] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023] Open
Abstract
The roles of physicists in medical imaging have expanded over the years, from the study of imaging systems (sources and detectors) and dose to the assessment of image quality and perception, the development of image processing techniques, and the development of image analysis methods to assist in detection and diagnosis. The latter is a natural extension of medical physicists' goals in developing imaging techniques to help physicians acquire diagnostic information and improve clinical decisions. Studies indicate that radiologists do not detect all abnormalities on images that are visible on retrospective review, and they do not always correctly characterize abnormalities that are found. Since the 1950s, the potential use of computers had been considered for analysis of radiographic abnormalities. In the mid-1980s, however, medical physicists and radiologists began major research efforts for computer-aided detection or computer-aided diagnosis (CAD), that is, using the computer output as an aid to radiologists-as opposed to a completely automatic computer interpretation-focusing initially on methods for the detection of lesions on chest radiographs and mammograms. Since then, extensive investigations of computerized image analysis for detection or diagnosis of abnormalities in a variety of 2D and 3D medical images have been conducted. The growth of CAD over the past 20 years has been tremendous-from the early days of time-consuming film digitization and CPU-intensive computations on a limited number of cases to its current status in which developed CAD approaches are evaluated rigorously on large clinically relevant databases. CAD research by medical physicists includes many aspects-collecting relevant normal and pathological cases; developing computer algorithms appropriate for the medical interpretation task including those for segmentation, feature extraction, and classifier design; developing methodology for assessing CAD performance; validating the algorithms using appropriate cases to measure performance and robustness; conducting observer studies with which to evaluate radiologists in the diagnostic task without and with the use of the computer aid; and ultimately assessing performance with a clinical trial. Medical physicists also have an important role in quantitative imaging, by validating the quantitative integrity of scanners and developing imaging techniques, and image analysis tools that extract quantitative data in a more accurate and automated fashion. As imaging systems become more complex and the need for better quantitative information from images grows, the future includes the combined research efforts from physicists working in CAD with those working on quantitative imaging systems to readily yield information on morphology, function, molecular structure, and more-from animal imaging research to clinical patient care. A historical review of CAD and a discussion of challenges for the future are presented here, along with the extension to quantitative image analysis.
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Affiliation(s)
- Maryellen L Giger
- Department of Radiology, University of Chicago, Chicago, Illinois 60637, USA.
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Karnan M, Thangavel K. Automatic detection of the breast border and nipple position on digital mammograms using genetic algorithm for asymmetry approach to detection of microcalcifications. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2007; 87:12-20. [PMID: 17543415 DOI: 10.1016/j.cmpb.2007.04.007] [Citation(s) in RCA: 23] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/11/2006] [Revised: 04/15/2007] [Accepted: 04/15/2007] [Indexed: 05/15/2023]
Abstract
The presence of microcalcifications in breast tissue is one of the most incident signs considered by radiologist for an early diagnosis of breast cancer, which is one of the most common forms of cancer among women. In this paper, the Genetic Algorithm (GA) is proposed for automatic look at commonly prone area the breast border and nipple position to discover the suspicious regions on digital mammograms based on asymmetries between left and right breast image. The basic idea of the asymmetry approach is to scan left and right images are subtracted to extract the suspicious region. The proposed system consists of two steps: First, the mammogram images are enhanced using median filter, normalize the image, at the pectoral muscle region is excluding the border of the mammogram and comparing for both left and right images from the binary image. Further GA is applied to magnify the detected border. The figure of merit is calculated to evaluate whether the detected border is exact or not. And the nipple position is identified using GA. The some comparisons method is adopted for detection of suspected area. Second, using the border points and nipple position as the reference the mammogram images are aligned and subtracted to extract the suspicious region. The algorithms are tested on 114 abnormal digitized mammograms from Mammogram Image Analysis Society database.
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Affiliation(s)
- M Karnan
- Department of Computer Science and Engineering, Tamil Nadu College of Engineering, Coimbatore, Tamil Nadu, India.
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Nakayama R, Uchiyama Y, Yamamoto K, Watanabe R, Namba K. Computer-aided diagnosis scheme using a filter bank for detection of microcalcification clusters in mammograms. IEEE Trans Biomed Eng 2006; 53:273-83. [PMID: 16485756 DOI: 10.1109/tbme.2005.862536] [Citation(s) in RCA: 74] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Mammography is considered the most effective method for early detection of breast cancers. However, it is difficult for radiologists to detect microcalcification clusters. Therefore, we have developed a computerized scheme for detecting early-stage microcalcification clusters in mammograms. We first developed a novel filter bank based on the concept of the Hessian matrix for classifying nodular structures and linear structures. The mammogram images were decomposed into several subimages for second difference at scales from 1 to 4 by this filter bank. The subimages for the nodular component (NC) and the subimages for the nodular and linear component (NLC) were then obtained from analysis of the Hessian matrix. Many regions of interest (ROIs) were selected from the mammogram image. In each ROI, eight features were determined from the subimages for NC at scales from 1 to 4 and the subimages for NLC at scales from 1 to 4. The Bayes discriminant function was employed for distinguishing among abnormal ROIs with a microcalcification cluster and two different types of normal ROIs without a microcalcification cluster. We evaluated the detection performance by using 600 mammograms. Our computerized scheme was shown to have the potential to detect microcalcification clusters with a clinically acceptable sensitivity and low false positives.
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Affiliation(s)
- Ryohei Nakayama
- Department of Radiology, Mie University School of Medicine, Tsu, Japan.
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Heinlein P, Drexl J, Schneider W. Integrated wavelets for enhancement of microcalcifications in digital mammography. IEEE TRANSACTIONS ON MEDICAL IMAGING 2003; 22:402-413. [PMID: 12760557 DOI: 10.1109/tmi.2003.809632] [Citation(s) in RCA: 32] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/24/2023]
Abstract
This paper presents a new algorithm for enhancement of microcalcifications in mammograms. The main novelty is the application of techniques we have developed for construction of filterbanks derived from the continuous wavelet transform. These discrete wavelet decompositions, called integrated wavelets, are optimally designed for enhancement of multiscale structures in images. Furthermore, we use a model based approach to refine existing methods for general enhancement of mammograms resulting in a more specific enhancement of microcalcifications. We present results of our method and compare them with known algorithms. Finally, we want to indicate how these techniques can also be applied to the detection of microcalcifications. Our algorithm was positively evaluated in a clinical study. It has been implemented in a mammography workstation designed for soft-copy reading of digital mammograms developed by IMAGETOOL, Germany.
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Affiliation(s)
- Peter Heinlein
- Zentrum Mathematik, Technische Universität München, Germany.
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Zhang W, Yoshida H, Nishikawa RM, Doi K. Optimally weighted wavelet transform based on supervised training for detection of microcalcifications in digital mammograms. Med Phys 1998; 25:949-56. [PMID: 9650185 DOI: 10.1118/1.598273] [Citation(s) in RCA: 47] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Abstract
We are developing a computer-aided diagnosis (CAD) scheme for detection of clustered microcalcifications in digital mammograms. The use of an empirically chosen wavelet and scale combination for detection of microcalcifications as an initial step of the CAD scheme has been reported by us previously. In this study, we developed a technique for optimizing the weights at individual scales in the wavelet transform to improve the performance of our CAD scheme based on the supervised learning method. In the learning process, an error function was formulated to represent the difference between a desired output and the reconstructed image obtained from weighted wavelet coefficients for a given mammogram. The error function was then minimized by modifying the weights for wavelet coefficients by means of a conjugate gradient algorithm. The Least Asymmetric Daubechies' wavelets were optimized with 297 regions of interest (ROIs) as a training set by a jackknife method. The performance of the optimally weighted wavelets was evaluated by means of receiver-operating characteristic (ROC) analysis by use of the above set of ROIs. The analysis yielded an average area under the ROC curve of 0.92, which outperforms the difference-image technique used in our existing CAD scheme, as well as the partial reconstruction method used in our previous study.
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Affiliation(s)
- W Zhang
- Kurt Rossmann Laboratories for Radiologic Image Research, Department of Radiology, University of Chicago, Illinois 60637, USA
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Kalman BL, Reinus WR, Kwasny SC, Laine A, Kotner L. Prescreening entire mammograms for masses with artificial neural networks: preliminary results. Acad Radiol 1997; 4:405-14. [PMID: 9189197 DOI: 10.1016/s1076-6332(97)80046-3] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
RATIONALE AND OBJECTIVES The authors evaluated the feasibility of combining wavelet transform and artificial neural network (ANN) technologies to prescreen mammograms for masses. METHODS AND MATERIALS Fifty-five mammograms (29 with masses and 26 without) were digitized to 100-mm resolution and processed by using wavelet transformation. These wavelets were subjected to a linear output sequential recursive auto-associative memory ANN and cluster analysis with feature vector formation. These vectors were used in two separate experiments-one with 13 cases and another with seven cases held out in a test set-to train feed-forward ANNs to detect the mammograms with a mass. The experiments were repeated with rerandomization of the data, four and six times, respectively. RESULTS There was a statistically significant correlation (P < .01) between the network's prediction of a mass and the presence of a mass. With majority voting, the feed-forward ANNs detected masses with 79% sensitivity and 50% specificity. CONCLUSION Although preliminary, the combination of wavelet transform and ANN is promising and may provide a viable method to prescreen mammograms for masses with high sensitivity and reasonable specificity.
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Affiliation(s)
- B L Kalman
- Mallinckrodt Institute of Radiology, Barnes-Jewish Hospital, St Louis, MO 63110, USA
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Clarençon D, Renaudin M, Gourmelon P, Kerckhoeve A, Catérini R, Boivin E, Ellis P, Hille B, Fatôme M. Real-time spike detection in EEG signals using the wavelet transform and a dedicated digital signal processor card. J Neurosci Methods 1996; 70:5-14. [PMID: 8982975 DOI: 10.1016/s0165-0270(96)00073-8] [Citation(s) in RCA: 14] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
This paper describes a complete real-time system for EEG signal analysis. Specific software and hardware have been designed to provide biologists with an efficient tool, which allows a complete study of the different states of vigilance as well as the paroxysmal activities. The analysis method which is based on the wavelet transform is first presented and compared to the standard spectral approach. The dedicated digital signal processor card, based on the Motorola 96002 processor chip, that has been designed to support real-time acquisition and real-time processing of EEG signals is then presented. We finally illustrate the proposed method by processing real EEG signals of rats, and show that it opens up new prospects in the domain of EEG-based diagnosis. We propose a new representation, called globalization, that provides a global view and better detection of paroxysmal activities.
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Affiliation(s)
- D Clarençon
- Unité de Radioprotection, CRSSA Emile Pardé, BP 87, La Tronche, France
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Yoshida H, Doi K, Nishikawa RM, Giger ML, Schmidt RA. An improved computer-assisted diagnostic scheme using wavelet transform for detecting clustered microcalcifications in digital mammograms. Acad Radiol 1996; 3:621-7. [PMID: 8796725 DOI: 10.1016/s1076-6332(96)80186-3] [Citation(s) in RCA: 61] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
Abstract
RATIONALE AND OBJECTIVES We evaluated the potential usefulness of a computer-assisted diagnostic (CAD) scheme incorporating the wavelet transform for detecting clustered microcalcifications in mammograms. METHODS A wavelet transform technique was applied to the detection of clustered microcalcifications. We examined several wavelets to study their effectiveness in detecting subtle microcalcifications. We used a database consisting of 39 mammograms containing 41 clusters of microcalcifications. The performance of the wavelet-based CAD scheme was evaluated using free-response receiver operating characteristic analysis. RESULTS The CAD scheme with the wavelet transform was useful in detecting some of the subtle microcalcifications that were not detected by our previous scheme, which was based on the difference-image technique. When the two schemes were combined, the overall performance was improved to a sensitivity of approximately 95%, with a false-positive rate of 1.5 clusters per image. CONCLUSION The wavelet transform approach can improve the detection of subtle clustered microcalcifications.
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Affiliation(s)
- H Yoshida
- Kurt Rossmann Laboratories for Radiologic Image Research, Department of Radiology, University of Chicago, IL 60637, USA
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Kallergi M, Clarke LP, Qian W, Gavrielides M, Venugopal P, Berman CG, Holman-Ferris SD, Miller MS, Clark RA. Interpretation of calcifications in screen/film, digitized, and wavelet-enhanced monitor-displayed mammograms: a receiver operating characteristic study. Acad Radiol 1996; 3:285-93. [PMID: 8796676 DOI: 10.1016/s1076-6332(96)80240-6] [Citation(s) in RCA: 32] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
Abstract
RATIONALE AND OBJECTIVES The acceptance of filmless digital mammography is currently limited by digitization and display drawbacks, as well as bias toward hard-copy interpretation. In the current study, we evaluated a wavelet-based image enhancement method for the filmless interpretation of breast calcifications. METHODS A set of 100 mammograms (58 with calcification clusters) was digitized at 105 microns and 4,096 gray levels per pixel and was processed with nonlinear filters and wavelets. Standard receiver operating characteristic analysis was performed by four radiologists, who independently read the films, the unprocessed digital images, and unprocessed and wavelet-enhanced digital images presented simultaneously. RESULTS Statistical differences were observed between screen/film and unprocessed digitized mammography displayed on monitors. Differences were not significant when wavelet enhancement was included in the monitor display. Interobserver variation in the digitized reading was greater than in film reading, but the wavelet enhancement reduced the difference. CONCLUSION Wavelet-enhanced digital mammograms may assist radiologists in diagnosing calcifications directly from computer monitors and may compensate for current technologic limitations. A study with a larger data-base is needed before this method is accepted for clinical use.
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Affiliation(s)
- M Kallergi
- Department of Radiology, College of Medicine, University of South Florida, Tampa 33612-4799, USA
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Zheng B, Qian W, Clarke LP. Digital mammography: mixed feature neural network with spectral entropy decision for detection of microcalcifications. IEEE TRANSACTIONS ON MEDICAL IMAGING 1996; 15:589-597. [PMID: 18215940 DOI: 10.1109/42.538936] [Citation(s) in RCA: 32] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/25/2023]
Abstract
A computationally efficient mixed feature based neural network (MFNN) is proposed for the detection of microcalcification clusters (MCCs) in digitized mammograms. The MFNN employs features computed in both the spatial and spectral domain and uses spectral entropy as a decision parameter. Backpropagation with Kalman filtering (KF) is employed to allow more efficient network training as required for evaluation of different features, input images, and related error analysis. A previously reported, wavelet-based image-enhancement method is also employed to enhance microcalcification clusters for improved detection. The relative performance of the MFNN for both the raw and enhanced images is evaluated using a common image database of 30 digitized mammograms, with 20 images containing 21 biopsy proven MCCs and ten normal cases. The computed sensitivity (true positive (TP) detection rate) was 90.1% with an average low false positive (FP) detection of 0.71 MCCs/image for the enhanced images using a modified k-fold validation error estimation technique. The corresponding computed sensitivity for the raw images was reduced to 81.4% and with 0.59 FP's MCCs/image. A relative comparison to an earlier neural network (NN) design, using only spatially related features, suggests the importance of the addition of spectral domain features when the raw image data is analyzed.
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Affiliation(s)
- B Zheng
- Dept. of Radiol., Univ. of South Florida, Tampa, FL
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Clarke LP, Velthuizen RP, Camacho MA, Heine JJ, Vaidyanathan M, Hall LO, Thatcher RW, Silbiger ML. MRI segmentation: methods and applications. Magn Reson Imaging 1995; 13:343-68. [PMID: 7791545 DOI: 10.1016/0730-725x(94)00124-l] [Citation(s) in RCA: 487] [Impact Index Per Article: 16.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
The current literature on MRI segmentation methods is reviewed. Particular emphasis is placed on the relative merits of single image versus multispectral segmentation, and supervised versus unsupervised segmentation methods. Image pre-processing and registration are discussed, as well as methods of validation. The application of MRI segmentation for tumor volume measurements during the course of therapy is presented here as an example, illustrating problems associated with inter- and intra-observer variations inherent to supervised methods.
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Affiliation(s)
- L P Clarke
- H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
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Wei Qian, Clarke L, Baoyu Zheng, Kallergi M, Clark R. Computer assisted diagnosis for digital mammography. ACTA ACUST UNITED AC 1995. [DOI: 10.1109/51.464772] [Citation(s) in RCA: 41] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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Lucier BJ, Kallergi M, Qian W, DeVore RA, Clark RA, Saff EB, Clarke LP. Wavelet compression and segmentation of digital mammograms. J Digit Imaging 1994; 7:27-38. [PMID: 8172976 DOI: 10.1007/bf03168476] [Citation(s) in RCA: 25] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023] Open
Abstract
An initial evaluation of Haar wavelets is presented in this study for the compression of mammographic images. Fifteen mammograms with 105 microns/pixel resolution and varying dynamic range (10 and 12 bits per pixel) containing clustered microcalcifications were compressed with two different rates. The quality and content of the compressed reconstructed images was evaluated by an expert mammographer. The visualization of the cluster was on the average good but degraded with increasing compression because of the discontinuities introduced by these types of wavelets as the compression rate increases. However, the artifacts in the decoded images were seen as totally artificial and were not misinterpreted by the radiologist as calcifications. The classification of the parenchymal densities did not change significantly but the morphology of the calcifications was increasingly distorted as the compression rate increased leading to lower estimates of the suspiciousness of the cluster and higher uncertainties in the diagnosis. The uncompressed and two sets of compressed images were also processed by a wavelet method to extract the calcifications. Despite the fact that the segmentation algorithm generated several false-positive signals in highly compressed images, all true clusters were successfully segmented indicating that the compression process preserved the features of interest. Our preliminary results indicated that wavelets could be used to achieve high compression rates of mammographic images without losing small details such as microcalcification clusters as well as detect the calcifications from either the uncompressed or compressed reconstructed data. Further research and application of multiresolution analysis to digital mammography is continuing.
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Affiliation(s)
- B J Lucier
- Department of Mathematics, Purdue University, W. Lafayette, IN
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