Detection of spicules on mammogram based on skeleton analysis

Existence of spicules is one of important clues of malignant tumors. This paper presents a new image processing method for the detection of spicules on mammogram. Spicules can be recognized as line patterns radiating from the center of tumor. To detect such characteristic patterns, line skeletons and a modified Hough transform are proposed. Line skeleton processing is effective in enhancing spinal axes of spicules and in reducing the other skeletons. The modified Hough transform is applied to line skeletons and radiating line structures are obtained. Experiments were made to test the performance of the proposed method. The system was designed using 19 training images, for which one normal case was recognized to be star-shaped. The other case were recognized correctly. Another experiments using 34 test images were also performed. The correct classification rate was 74%. These results shows the effectiveness of the proposed method.

[1]  Shigeru Nawano,et al.  Computer Diagnosis of Breast Cancer by Mammogram Processing , 1993 .

[2]  Jean Serra,et al.  Image Analysis and Mathematical Morphology , 1983 .

[3]  Y. Wu,et al.  Artificial neural networks in mammography: application to decision making in the diagnosis of breast cancer. , 1993, Radiology.

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

[5]  Kunio Doi,et al.  Investigation of methods for the computerized detection and analysis of mammographic masses , 1990, Medical Imaging: Image Processing.

[6]  L V Ackerman,et al.  Computer screening of xeromammograms: a technique for defining suspicious areas of the breast. , 1979, Computers and biomedical research, an international journal.

[7]  M L Giger,et al.  Computers aid diagnosis of breast abnormalities. , 1993, Diagnostic imaging.

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

[9]  H. Kobatake,et al.  Automatic detection of malignant tumors on mammogram , 1994, Proceedings of 1st International Conference on Image Processing.

[10]  Jack Sklansky,et al.  Automatic Detection of Suspicious Abnormalities in Breast Radiographs , 1977 .

[11]  M L Giger,et al.  Computerized detection of masses in digital mammograms: analysis of bilateral subtraction images. , 1991, Medical physics.

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

[13]  M. Yaffe,et al.  Characterisation of mammographic parenchymal pattern by fractal dimension. , 1990, Physics in medicine and biology.

[14]  W F Bischof,et al.  Automated detection and classification of breast tumors. , 1992, Computers and biomedical research, an international journal.