Towards automated segmentation and classification of masses in mammograms

This work presents a straightforward approach to detecting and segmenting mammographic mass cores. The method utilizes adaptive thresholding applied to a contrast-enhanced version of the gray-scale mammogram, where the threshold is a function of the localized gray-level mean and variance. To assess the method's efficacy, it is applied to a database of 62 mammograms, each containing a suspicious mass (39 benign and 23 malignant). Each test case consists of a gray-scale image and a binary image containing a radiologist segmentation of the mass. After segmentation, a variety of features are extracted, including several based on the normalized radial length, rubber band straightening algorithm, gray-level statistics, and patient age. Next, step-wise linear discriminant analysis is utilized for feature reduction and optimization. The same procedure is applied to the manually segmented masses. Analysis of the optimized features resulted in an ROC curve area of Az = 0.8796 and Az = 0.8719 for the automated and manually segmented masses, respectively.

[1]  Hao Jiang,et al.  Detection of spicules in mammograms , 1997, Proceedings of International Conference on Image Processing.

[2]  Sheng Liu,et al.  Multiresolution detection of spiculated lesions in digital mammograms , 2001, IEEE Trans. Image Process..

[3]  Díbio Leandro Borges,et al.  Automated mammogram classification using a multiresolution pattern recognition approach , 2001, Proceedings XIV Brazilian Symposium on Computer Graphics and Image Processing.

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

[5]  L. Bruce,et al.  Classifying mammographic mass shapes using the wavelet transform modulus-maxima method , 1999, IEEE Transactions on Medical Imaging.

[6]  Richard H. Moore,et al.  Current Status of the Digital Database for Screening Mammography , 1998, Digital Mammography / IWDM.

[7]  Berkman Sahiner,et al.  Computer-aided characterization of mammographic masses: accuracy of mass segmentation and its effects on characterization , 2001, IEEE Transactions on Medical Imaging.

[8]  Alicia Samuels,et al.  Cancer Statistics, 2003 , 2003, CA: a cancer journal for clinicians.

[9]  M. Sameti,et al.  Texture feature extraction for tumor detection in mammographic images , 1997, 1997 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing, PACRIM. 10 Years Networking the Pacific Rim, 1987-1997.

[10]  Ralph D. Semmel,et al.  An algorithm for early breast cancer detection in mammograms , 1992, [1992] Proceedings Fifth Annual IEEE Symposium on Computer-Based Medical Systems.

[11]  Thomas G. Dietterich.  What is machine learning? , 2020, Archives of Disease in Childhood.

[12]  N. Petrick,et al.  Improvement of radiologists' characterization of mammographic masses by using computer-aided diagnosis: an ROC study. , 1999, Radiology.

[13]  Finding Breast Cancer Early , 2003, CA: a cancer journal for clinicians.

[14]  Nasser Kehtarnavaz,et al.  Texture based classification of mass abnormalities in mammograms , 2000, Proceedings 13th IEEE Symposium on Computer-Based Medical Systems. CBMS 2000.