Breast Cancer: Importance of Spiculation in Computer-aided Detection

Vyborny, Carl J.; Doi, Takeshi; O’Shaughnessy, Kathryn F.; Romsdahl, Harlan; Schneider, Alexander C.; Stein, Alan

doi:10.1148/radiology.215.3.r00jn38703

Cited by 64 publications

(25 citation statements)

References 14 publications

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“…Unlike previously developed computer schemes that detect and classify spiculated and nonspiculated masses [18][19][20][21], our scheme uses a simple summary index to quantify spiculation levels of any suspected masses. As do most of current CAD schemes, our scheme used the low-resolution image to define the initial boundary contour of a suspected mass, thereby reducing image noise and increasing the computation efficiency of the region growth algorithm.…”

Section: Discussionmentioning

confidence: 99%

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Interactive Computer-Aided Diagnosis of Breast Masses: Computerized Selection of Visually Similar Image Sets From a Reference Library

et al. 2007

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Rationale and Objectives: The clinical utility of interactive computer-aided diagnosis (ICAD) systems depends on clinical relevance and visual similarity between the queried breast lesions and the ICAD-selected reference regions. The objective of this study is to develop and test a new ICAD scheme that aims improve visual similarity of ICAD-selected reference regions. Materials and Methods:A large and diverse reference library involving 3000 regions of interests was established. For each queried breast mass lesion by the observer, the ICAD scheme segments the lesion, classifies its boundary spiculation level, and computes 14 image features representing the segmented lesion and its surrounding tissue background. A conditioned k-nearest neighbor algorithm is applied to select a set of the 25 most "similar" lesions from the reference library. After computing the mutual information between the queried lesion and each of these initially selected 25 lesions, the scheme displays the six reference lesions with the highest mutual information scores. To evaluate the automated selection process of the six "visually similar" lesions to the queried lesion, we conducted a two-alternative forced-choice observer preference study using 85 queried mass lesions. Two sets of reference lesions selected by one new automated ICAD scheme and the other previously reported scheme using a subjective rating method were randomly displayed on the left and right side of the queried lesion. Nine observers were asked to decide for each of the 85 queried lesions which one of the two reference sets was "more visually similar" to the queried lesion. Results:In classification of mass boundary spiculation levels, the overall agreement rate between the automated scheme and an observer is 58.8% (Kappa = 0.31). In observer preference study, the nine observers preferred on average the reference lesion sets selected by the automated scheme as being more visually similar than the set selected by the subjective rating approach in 53.2% of the queried lesions. The results were not significantly different for the two methods (p = 0.128). Conclusion:This study suggests that using the new automated ICAD scheme, the inter-observer variability related issues can thus be avoided. Furthermore, the new scheme maintains the similar performance level as the previous scheme using the subjective rating method that can select reference sets that are significantly more visually similar (p < 0.05) than when using traditional ICAD schemes in which the mass boundary spiculation levels are not accurately detected and quantified.

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Section: Discussionmentioning

confidence: 99%

“…The visualized spiculation level of mass boundary has been well recognized as an important factor in classification between malignant and benign masses [18]. Several techniques have been developed and tested to detect and classify between spiculated and non-spiculated masses [19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Interactive Computer-Aided Diagnosis of Breast Masses: Computerized Selection of Visually Similar Image Sets From a Reference Library

et al. 2007

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“…Spiculated masses carry a much higher risk of malignancy than calcifications or other types of masses. The performance figures for the leading mass detection algorithms are not as good as those for microcalcification detection algorithms [7]. Masses appear as ill-defined local increases in brightness, are highly variable in appearance, and share many characteristics with normal background tissue.…”

Section: Introductionmentioning

confidence: 94%

“…Masses appear as ill-defined local increases in brightness, are highly variable in appearance, and share many characteristics with normal background tissue. Almost 50% of malignant masses are, however, characterized by a radial pattern of linear structures known as spicules [7,8]. In this paper, we present a computational technique that detects the spiculations in masses in ultrasound images.…”

Section: Introductionmentioning

confidence: 99%

Classification of Mass in Breast Ultrasound Images using Image Processing Techniques

Minavathi¹,

Murali²,

Dinesh³

2012

IJCA

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This work presents a new approach for classifying masses in breast ultrasound images. Detection and classification of masses in ultrasound images still remains a challenge because most of the ultrasound images contain speckle noise and fuzzy boundaries. Ultrasound (US) is an important adjunct to mammography in breast cancer detection as it increases the rate of detection in dense breasts. Ultrasound also does dynamic analysis of moving structures in breast thus it is used to analyze the functional behavior of breast. In the proposed method, ultrasound images are preprocessed using Gaussian smoothing to remove additive noise and anisotropic diffusion filters to remove multiplicative noise (speckle noise). Active contour method has been used to extract a closed contour of filtered image which is the boundary of the spiculated mass. Spiculations which make breast mass unstructured or irregular are marked by measuring the angle of curvature of each pixel at the boundary of mass. To classify the breast mass as malignant or benign we have used: the structure of mass in accordance with spiculations, elliptical shape of the mass and acoustic shadowing feature which is an important functional feature. We have used receiver operating characteristic curve (ROC) to evaluate the performance. We have validated the proposed algorithm on 100 sub images(40 spiculated and 60 non spiculated) and results shows 92.7% of sensitivity with 0.88 Area Under Curve. Proposed techniques were compared and contrasted with the existing methods and result demonstrates that proposed algorithm has successfully detected and classified mass ROI candidates in breast ultrasound images.

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“…20,21 Detecting spiculation as a part of mass region is thus essential for further computer analysis. In this task, a steerable ridge detection approach 22 was employed and it was further generalized into a multiscale analysis framework to detect the presence of spiculations.…”

Section: Iib3 Spiculation Detectionmentioning

confidence: 99%

Multilevel learning‐based segmentation of ill‐defined and spiculated masses in mammograms

Tao

Freedman

et al. 2010

Medical Physics

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Purpose: A learning-based approach integrating the use of pixel-level statistical modeling and spiculation detection is presented for the segmentation of mammographic masses with ill-defined margins and spiculations. Methods: The algorithm involves a multiphase pixel-level classification, using a comprehensive group of features computed from regional intensity, shape, and textures, to generate a massconditional probability map ͑PM͒. Then, the mass candidate, along with the background clutters consisting of breast fibroglandular and other nonmass tissues, is extracted from the PM by integrating the prior knowledge of shape and location of masses. A multiscale steerable ridge detection algorithm is employed to detect spiculations. Finally, all the object-level findings, including mass candidate, detected spiculations, and clutters, along with the PM, are integrated by graph cuts to generate the final segmentation mask.Results: The method was tested on 54 masses ͑51 malignant and 3 benign͒, all with ill-defined margins and irregular shape or spiculations. The ground truth delineations were provided by five experienced radiologists. Area overlapping ratio of 0.689 ͑Ϯ0.160͒ and 0.540 ͑Ϯ0.164͒ were obtained for segmenting entire mass and margin portion only, respectively. Williams index of area and contour based measurements indicated that the segmentation results of the algorithm agreed well with the radiologists' delineation. Conclusions: The proposed approach could closely delineate the mass body. Most importantly, it is capable of including mass margin and its spicule extensions which are considered as key features for breast lesion analyses.

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Breast Cancer: Importance of Spiculation in Computer-aided Detection

Cited by 64 publications

References 14 publications

Interactive Computer-Aided Diagnosis of Breast Masses: Computerized Selection of Visually Similar Image Sets From a Reference Library

Interactive Computer-Aided Diagnosis of Breast Masses: Computerized Selection of Visually Similar Image Sets From a Reference Library

Classification of Mass in Breast Ultrasound Images using Image Processing Techniques

Multilevel learning‐based segmentation of ill‐defined and spiculated masses in mammograms

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