Improved dynamic-programming-based algorithms for segmentation of masses in mammograms

• Published in: Medical physics (Lancaster) Vol. 34; no. 11; pp. 4256 - 4269
• Main Authors: Domínguez, Alfonso Rojas, Nandi, Asoke K.
• Format: Journal Article
• Language: English
• Published: United States American Association of Physicists in Medicine 01.11.2007
• Subjects: ACCURACY; ALGORITHMS; biological organs; BIOMEDICAL RADIOGRAPHY; Breast - pathology; breast cancer; breast masses; Breast Neoplasms - diagnostic imaging; Breast Neoplasms - metabolism; Breast Neoplasms - pathology; CARCINOMAS; Computer aided diagnosis; COST; DESIGN; Digital mammography; Diseases; DYNAMIC PROGRAMMING; Humans; Image Enhancement; IMAGE PROCESSING; Image Processing, Computer-Assisted; Image quality; Image reconstruction; image segmentation; MAMMARY GLANDS; Mammography; Mammography - methods; medical image processing; Medical image reconstruction; Medical image segmentation; Medical image smoothing; Medical imaging; Models, Statistical; Numerical optimization; Pattern Recognition, Automated; Radiographic Image Interpretation, Computer-Assisted; Radiologists; RADIOLOGY AND NUCLEAR MEDICINE; Reproducibility of Results; Software; breast masses; dynamic programming; breast cancer; mammography; image segmentation
• ISSN: 0094-2405; 2473-4209
• DOI: 10.1118/1.2791034

In this paper, two new boundary tracing algorithms for segmentation of breast masses are presented. These new algorithms are based on the dynamic programming-based boundary tracing (DPBT) algorithm proposed in Timp and Karssemeijer, [S. Timp and N. Karssemeijer, Med. Phys. 31, 958–971 (2004)] The DPBT algorithm contains two main steps: (1) construction of a local cost function, and (2) application of dynamic programming to the selection of the optimal boundary based on the local cost function. The validity of some assumptions used in the design of the DPBT algorithm is tested in this paper using a set of 349 mammographic images. Based on the results of the tests, modifications to the computation of the local cost function have been designed and have resulted in the Improved-DPBT (IDPBT) algorithm. A procedure for the dynamic selection of the strength of the components of the local cost function is presented that makes these parameters independent of the image dataset. Incorporation of this dynamic selection procedure has produced another new algorithm which we have called ID 2 PBT . Methods for the determination of some other parameters of the DPBT algorithm that were not covered in the original paper are presented as well. The merits of the new IDPBT and ID 2 PBT algorithms are demonstrated experimentally by comparison against the DPBT algorithm. The segmentation results are evaluated with base on the area overlap measure and other segmentation metrics. Both of the new algorithms outperform the original DPBT; the improvements in the algorithms performance are more noticeable around the values of the segmentation metrics corresponding to the highest segmentation accuracy, i.e., the new algorithms produce more optimally segmented regions, rather than a pronounced increase in the average quality of all the segmented regions.