Fast detection of masses in computer-aided mammography

We present a complete method for fast detection of circumscribed mass in mammograms employing a radial basis function neural network (RBFNN). This method can distinguish between tumorous and healthy tissue among various parenchyma tissue patterns, making a decision whether a mammogram is normal or not, and then detecting the masses' position by performing sub-image windowing analysis. In the latter case, with the implementation of a set of criteria, square regions containing the masses are marked as regions of suspicion (ROS). Fast feature extraction significantly reduces the overall processing time, allowing implementation of the method on low-cost PCs. A detailed description of the proposed method is given. The computational efficiency of the feature extraction module and the neural classifier is derived, followed by an analysis of the effects of various factors such as the size of the window shifting step, the digitization quality, and the mammogram size on the computational effort. In addition, the data set and experimental results are presented.

[1]  M. Moskowitz,et al.  Breast cancer missed by mammography. , 1979, AJR. American journal of roentgenology.

[2]  M. Giger,et al.  Computer vision and artificial intelligence in mammography. , 1994, AJR. American journal of roentgenology.

[3]  Vijay K. Jain,et al.  Markov random field for tumor detection in digital mammography , 1995, IEEE Trans. Medical Imaging.

[4]  S. Lai,et al.  On techniques for detecting circumscribed masses in mammograms. , 1989, IEEE transactions on medical imaging.

[5]  Michael Brady,et al.  Finding Curvilinear Structures in Mammograms , 1995, CVRMed.

[6]  N. Petrick,et al.  Computerized analysis of mammographic microcalcifications in morphological and texture feature spaces. , 1998, Medical physics.

[7]  Gwo Giun Lee,et al.  Image segmentation using multiresolution wavelet analysis and expectation‐maximization (EM) algorithm for digital mammography , 1997 .

[8]  Lester Kallsher,et al.  Factors Influencing False Negative Rates in Xeromammography , 1979 .

[9]  J. Forgie,et al.  Results Obtained from a Vowel Recognition Computer Program , 1959 .

[10]  Paul Scheunders,et al.  Detection of microcalcifications using neural networks , 1996 .

[11]  James W. Modestino,et al.  A Maximum Likelihood Approach to Texture Classification , 1982, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[12]  M. Wallis,et al.  A review of false negative mammography in a symptomatic population. , 1991, Clinical radiology.

[13]  Atam P. Dhawan,et al.  Radial-basis-function based classification of mammographic microcalcifications using texture features , 1995, Proceedings of 17th International Conference of the Engineering in Medicine and Biology Society.

[14]  Michael Brady,et al.  A Representation for Mammographic Image Processing , 1995, CVRMed.

[15]  K. J. Ray Liu,et al.  Fractal modeling and segmentation for the enhancement of microcalcifications in digital mammograms , 1997, IEEE Transactions on Medical Imaging.

[16]  Berkman Sahiner,et al.  An adaptive density-weighted contrast enhancement filter for mammographic breast mass detection , 1996, IEEE Trans. Medical Imaging.

[17]  D. Brzakovic,et al.  MAMMOGRAM SCREENING USING MULTIRESOLUTION-BASED IMAGE SEGMENTATION , 1993 .

[18]  K Doi,et al.  Image feature analysis and computer-aided diagnosis in digital radiography. I. Automated detection of microcalcifications in mammography. , 1987, Medical physics.

[19]  Michael Unser,et al.  Sum and Difference Histograms for Texture Classification , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[20]  Maryellen L. Giger.  Computer-aided diagnosis in digital mammography , 1993, Proceedings of the 15th Annual International Conference of the IEEE Engineering in Medicine and Biology Societ.

[21]  C J Vyborny,et al.  Can computers help radiologists read mammograms? , 1994, Radiology.

[22]  Nicolaos B. Karayiannis,et al.  Detection of microcalcifications in digital mammograms using wavelets , 1998, IEEE Transactions on Medical Imaging.

[23]  E. Sickles.  Breast calcifications: mammographic evaluation. , 1986, Radiology.

[24]  Kevin Knight,et al.  Connectionist ideas and algorithms , 1990, CACM.

[25]  Susan M. Astley,et al.  Classification of breast tissue by texture analysis , 1992, Image Vis. Comput..

[26]  J L Semmlow,et al.  A fully automated system for screening xeromammograms. , 1980, Computers and biomedical research, an international journal.

[27]  N. Petrick,et al.  Computer-aided classification of mammographic masses and normal tissue: linear discriminant analysis in texture feature space. , 1995, Physics in medicine and biology.

[28]  Berkman Sahiner,et al.  Classification of mass and normal breast tissue: a convolution neural network classifier with spatial domain and texture images , 1996, IEEE Trans. Medical Imaging.

[29]  J. M. Pruneda,et al.  Computer-aided mammographic screening for spiculated lesions. , 1994, Radiology.

[30]  Anke Meyer-Bäse,et al.  Multiscale segmentation through a radial basis neural network , 1997, Proceedings of International Conference on Image Processing.

[31]  Martin P. DeSimio,et al.  Computer-aided breast cancer detection and diagnosis of masses using difference of Gaussians and derivative-based feature saliency , 1997, IEEE Transactions on Medical Imaging.

[32]  D B Kopans,et al.  Discriminating analysis uncovers breast lesions. , 1991, Diagnostic imaging.

[33]  H Yoshida,et al.  Optimally weighted wavelet transform based on supervised training for detection of microcalcifications in digital mammograms. , 1998, Medical physics.

[34]  Evangelos Dermatas,et al.  Neural classification of abnormal tissue in digital mammography using statistical features of the texture , 1999, ICECS'99. Proceedings of ICECS '99. 6th IEEE International Conference on Electronics, Circuits and Systems (Cat. No.99EX357).

[35]  Wei Qian,et al.  Image feature extraction for mass detection in digital mammography: Influence of wavelet analysis , 1999 .

[36]  Nico Karssemeijer,et al.  ADAPTIVE NOISE EQUALIZATION AND RECOGNITION OF MICROCALCIFICATION CLUSTERS IN MAMMOGRAMS , 1993 .

[37]  D Brzakovic,et al.  An approach to automated detection of tumors in mammograms. , 1990, IEEE transactions on medical imaging.

[38]  De Luca Jt,et al.  Mammography in clinical practice. , 1966 .