Identification and segmentation of obscure pectoral muscle in mediolateral oblique mammograms.

OBJECTIVE: X-ray mammography is a widely used and reliable method for detecting pre-symptomatic breast cancer. One of the difficulties in automatically computerized mammogram analysis is the presence of pectoral muscles in mediolateral oblique mammograms because the pectoral muscle does not belong to the scope of the breast. The objective of this study is to identify the boundary of obscure pectoral muscle in mediolateral oblique mammograms. METHODS: Two tentative boundary curves are individually created to be the potential boundaries. To find the first tentative boundary, this study finds local extrema, prunes weak extrema and then determines an appropriate threshold for identifying the brighter tissue, whose edge is considered the first tentative boundary. The second tentative boundary is found by partitioning the breast into several regions, where each local threshold is tuned based on the local intensity. Subsequently, both of these tentative boundaries are used as the reference to create a refined boundary by Hough transform. Then, the refined boundary is partitioned into quadrilateral regions, in which the edge of this boundary is detected. Finally, these reliable edge points are collected to generate the genuine boundary by curve fitting. RESULTS: The proposed method achieves the least mean square error 4.88 ± 2.47 (mean ± standard deviation) and the least misclassification error rate (MER) with 0.00466 ± 0.00191 in terms of MER. CONCLUSION: The experimental results indicate that this method performs best and stably in boundary identification of the pectoral muscle. ADVANCES IN KNOWLEDGE: The proposed method can identify the boundary from obscure pectoral muscle, which has not been solved by the previous studies.

Identification of breast contour for nipple segmentation in breast magnetic resonance images.

PURPOSE: The purpose of this study is to develop a method to simulate the breast contour and segment the nipple in breast magnetic resonance images. METHODS: This study first identifies the chest wall and removes the chest part from the breast MR images. Subsequently, the cleavage and its motion artifacts are removed, distinguishing the separate breasts, where the edge points are sampled for curve fitting. Next, a region growing method is applied to find the potential nipple region. Finally, the potential nipple region above the simulated curve can be removed in order to retain the original smooth contour. RESULTS: The simulation methods can achieve the least root mean square error (RMSE) for certain cases. The proposed YBnd and (Dmin+Dmax)/2 methods are significant due toP = 0.000. The breast contour curve detected by the two proposed methods is closer than that determined by the edge detection method. The (Dmin+Dmax)/2 method can achieve the lowest RMSE of 1.1029 on average, while the edge detection method results in the highest RMSE of 6.5655. This is only slighter better than the comparison methods, which implies that the performance of these methods depends upon the conditions of the cases themselves. Under this method, the maximal Dice coefficient is 0.881, and the centroid difference is 0.36 pixels. CONCLUSIONS: The contributions of this study are twofold. First, a method was proposed to identify and segment the nipple in breast MR images. Second, a curve-fitting method was used to simulate the breast contour, allowing the breast to retain its original smooth shape.

Detection and construction of chest wall on breast magnetic resonance images.

PURPOSE: The purpose of this study is to propose a method for detection and construction of chest wall for breast magnetic resonance images. METHODS: A volume of breast MR slices are firstly acquired and utilized to detect initial points of chest wall. To calibrate the chest wall curve, the points along the curve is set with reference to its neighboring points. Through the calibration method, a curve of chest wall can be detected from a volume of breast magnetic resonance (MR) slices. Such a curve can be applied for segmentation of breast region in a volume of MR images. RESULTS: The experimental results reveal that the minimal RMSE was measured from the setting two polynomial functions and the points from the vertical position ≤320. If all edge points are used to simulate the curve, two circle functions can reach the minimal RMSE. CONCLUSION: The experimental results verify that chest wall for breast density estimation can be better simulated by two circle functions, which simulate right and left chest walls respectively. Furthermore, such a simulation curve is suggested to utilize partial edge points under the given vertical position.

Estimation of breast density: an adaptive moment preserving method for segmentation of fibroglandular tissue in breast magnetic resonance images.

PURPOSE: Breast density has been found to be a potential indicator for breast cancer risk. The estimation of breast density can be seen as a segmentation problem on fibroglandular tissues from a breast magnetic resonance image. The classic moment preserving is a thresholding method, which can be applied to determine an appropriate threshold value for fibroglandular tissue segmentation. METHODS: This study proposed an adaptive moment preserving method, which combines the classic moment preserving and a thresholding adjustment method. The breast MR images are firstly performed to extract the fibroglandular tissue from the breast tissue. The next step is to obtain the areas of the fibroglandular tissue and the whole breast tissue. Finally, breast density can be estimated for the given breast. RESULTS: The Friedman test shows that the qualities of segmentation are insignificant with p<0.000 and Friedman chi-squared=1116.12. The Friedman test shows that there would be significant differences in the sum of the ranks of at least one segmentation method. Average ranks indicate that the performance of the four methods is ranked as adaptive moment preserving, fuzzy c-means, moment preserving, and Kapur's method in order. Among the four methods, adaptive moment preserving also achieves the minimum values of MAE and RMSE with 9.2 and 12. CONCLUSION: This study has verified that the proposed adaptive moment preserving can identify and segment the fibroglandular tissues from the 2D breast MR images and estimate the degrees of breast density.

Mammogram retrieval through machine learning within BI-RADS standards.

A content-based mammogram retrieval system can support usual comparisons made on images by physicians, answering similarity queries over images stored in the database. The importance of searching for similar mammograms lies in the fact that physicians usually try to recall similar cases by seeking images that are pathologically similar to a given image. This paper presents a content-based mammogram retrieval system, which employs a query example to search for similar mammograms in the database. In this system the mammographic lesions are interpreted based on their medical characteristics specified in the Breast Imaging Reporting and Data System (BI-RADS) standards. A hierarchical similarity measurement scheme based on a distance weighting function is proposed to model user's perception and maximizes the effectiveness of each feature in a mammographic descriptor. A machine learning approach based on support vector machines and user's relevance feedback is also proposed to analyze the user's information need in order to retrieve target images more accurately. Experimental results demonstrate that the proposed machine learning approach with Radial Basis Function (RBF) kernel function achieves the best performance among all tested ones. Furthermore, the results also show that the proposed learning approach can improve retrieval performance when applied to retrieve mammograms with similar mass and calcification lesions, respectively.

Can breast magnetic resonance imaging demonstrate characteristic findings of preoperative ductal carcinoma in situ in Taiwanese women?

OBJECTIVE: Magnetic resonance imaging (MRI) is more sensitive than mammography and sonography for breast cancer detection, but its diagnostic specificity is still being debated, and standardised criteria are lacking. METHODS: This study used a dedicated breast MRI system with a Spiral RODEO pulse sequence, and applied postprocessing techniques including multiplanar reformation (MPR) with ductal orientation, early subtracted phase (ESP) and a postcontrast kinetic curve. We discuss the possible MRI/pathology correlations based on pathogenetic concepts. We retrospectively collected data from 13 cases of benign intraductal and early-stage malignant lesions to observe the capability of MPR, ESP and kinetic curve techniques to diagnose early lesions differentially. MRI features and pathological findings for these cases were collected. RESULTS: Thirteen cases of ductal carcinoma in situ with MRI characteristics and pathological findings were identified. We analysed early ductal lesions, such as intraductal epithelial hyperplasia, intraductal papilloma, ductal carcinoma in situ and small focal invasive ductal carcinoma. Using MRI with MPR to demonstrate ductal orientation, we found 12 cases with a ductogram appearance and 6 with crossing-over glandular tissue. The tumour size estimated by MRI was accurate in 6 cases, but overestimated in seven. CONCLUSION: Dedicated breast MRI with MPR, ESP and kinetic curve analyses might be helpful in defining some characteristics of early-stage malignant lesions.