Breast Mass Segmentation in Mammography Using Plane Fitting and Dynamic Programming

Song, Enmin; Jiang, Luan; Jin, Renchao; Zhang, Lin; Yuan, Yuan; Li, Qiang

doi:10.1016/j.acra.2008.11.014

Cited by 44 publications

(25 citation statements)

References 26 publications

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“…The methods of DPBT and PFDP were implemented on the basis of the descriptions in Timp and Karssemeijer's study [8] and Song et al's study [10], respectively. Figure 4 shows six results of these segmentation methods: the lesions of the left three columns are benign, and the other three are malignant.…”

Section: Resultsmentioning

confidence: 99%

“…We use the area overlap metric [9,10,25], the Hausdorff distance metric, and the average minimum Euclidean distance metric [26][27][28] to quantify the consistency between the segmented results and the ground truth provided by a radiologist's manually delineated outlines. The AOM metric is defined as the ratio of the intersection to the union of the two areas to be compared:…”

Section: Discussionmentioning

confidence: 99%

“…Rojas Domínguez and Nandi modified the local cost function and designed two improved DPBT methods [9]. Song et al segmented breast lesions using plane fitting and dynamic programming (PFDP) [10]. First, the edge candidate points were obtained using a plane fitting method.…”

Section: Introductionmentioning

confidence: 99%

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Marker-Controlled Watershed for Lesion Segmentation in Mammograms

Liu

Song

2011

J Digit Imaging

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Lesion segmentation, which is a critical step in computer-aided diagnosis system, is a challenging task as lesion boundaries are usually obscured, irregular, and low contrast. In this paper, an accurate and robust algorithm for the automatic segmentation of breast lesions in mammograms is proposed. The traditional watershed transformation is applied to the smoothed (by the morphological reconstruction) morphological gradient image to obtain the lesion boundary in the belt between the internal and external markers. To automatically determine the internal and external markers, the rough region of the lesion is identified by a template matching and a thresholding method. Then, the internal marker is determined by performing a distance transform and the external marker by morphological dilation. The proposed algorithm is quantitatively compared to the dynamic programming boundary tracing method and the plane fitting and dynamic programming method on a set of 363 lesions (size range, 5-42 mm in diameter; mean, 15 mm), using the area overlap metric (AOM), Hausdorff distance (HD), and average minimum Euclidean distance (AMED). The mean ± SD of the values of AOM, HD, and AMED for our method were respectively 0.72 ± 0.13, 5.69 ± 2.85 mm, and 1.76 ± 1.04 mm, which is a better performance than two other proposed segmentation methods. The results also confirm the potential of the proposed algorithm to allow reliable segmentation and quantification of breast lesion in mammograms.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Marker-Controlled Watershed for Lesion Segmentation in Mammograms

Liu

Song

2011

J Digit Imaging

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“…We also assessed quantitative measurements: contour distance-based measurements such as Hausdorff distance, 22 and area matching-based measurements, e.g., over-estimation and under-estimation of the area our segmentation compared to the "reference standard." 10,14 Agreement between our segmentation and the manual "reference standard" was assessed by concordance correlation 23 and the Bland-Altman analysis 24 of the change in SLD. We compared the percentage changes in SLD from baseline to "reference standard" and automated segmentation.…”

Section: Algorithm Evaluationmentioning

confidence: 99%

“…7-9)], and breast masses [e.g., (Refs. [10][11][12]]. To our knowledge, there is one main study that focused on tracking and segmentation of lymph nodes.…”

Section: Introductionmentioning

confidence: 99%

Automated temporal tracking and segmentation of lymphoma on serial CT examinations

Greenspan

Napel

et al. 2011

Medical Physics

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Purpose: It is challenging to reproducibly measure and compare cancer lesions on numerous follow-up studies; the process is time-consuming and error-prone. In this paper, we show a method to automatically and reproducibly identify and segment abnormal lymph nodes in serial computed tomography (CT) exams. Methods: Our method leverages initial identification of enlarged (abnormal) lymph nodes in the baseline scan. We then identify an approximate region for the node in the follow-up scans using nonrigid image registration. The baseline scan is also used to locate regions of normal, non-nodal tissue surrounding the lymph node and to map them onto the follow-up scans, in order to reduce the search space to locate the lymph node on the follow-up scans. Adaptive region-growing and clustering algorithms are then used to obtain the final contours for segmentation. We applied our method to 24 distinct enlarged lymph nodes at multiple time points from 14 patients. The scan at the earlier time point was used as the baseline scan to be used in evaluating the follow-up scan, resulting in 70 total test cases (e.g., a series of scans obtained at 4 time points results in 3 test cases). For each of the 70 cases, a "reference standard" was obtained by manual segmentation by a radiologist. Assessment according to response evaluation criteria in solid tumors (RECIST) using our method agreed with RECIST assessments made using the reference standard segmentations in all test cases, and by calculating node overlap ratio and Hausdorff distance between the computer and radiologist-generated contours. Results: Compared to the reference standard, our method made the correct RECIST assessment for all 70 cases. The average overlap ratio was 80.7 6 9.7% s.d., and the average Hausdorff distance was 3.2 6 1.8 mm s.d. The concordance correlation between automated and manual segmentations was 0.978 (95% confidence interval 0.962, 0.984). The 100% agreement in our sample between our method and the standard with regard to RECIST classification suggests that the true disagreement rate is no more than 6%. Conclusions: Our automated lymph node segmentation method achieves excellent overall segmentation performance and provides equivalent RECIST assessment. It potentially will be useful to streamline and improve cancer lesion measurement and tracking and to improve assessment of cancer treatment response.

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Benefits of Screening Mammography

Berg¹

2010

JAMA

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Breast Mass Segmentation in Mammography Using Plane Fitting and Dynamic Programming

Cited by 44 publications

References 26 publications

Marker-Controlled Watershed for Lesion Segmentation in Mammograms

Marker-Controlled Watershed for Lesion Segmentation in Mammograms

Automated temporal tracking and segmentation of lymphoma on serial CT examinations

Benefits of Screening Mammography

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