Computer-aided mammographic screening for spiculated lesions.

Kegelmeyer, W. Philip; Pruneda, J M; Bourland, Philip D.; Hillis, Argye; Riggs, Mark; Nipper, Michael L.

doi:10.1148/radiology.191.2.8153302

Cited by 267 publications

(132 citation statements)

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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%

Section: Introductionmentioning

confidence: 99%

“…Several techniques have been developed and tested to detect and classify between spiculated and non-spiculated masses [19][20][21]. One group used the analysis of locally oriented edges (ALOE) and a binary decision tree (BDT) [19]. Since spiculation frequently appears as linear structures with a positive image contrast and the structures lie in all radial directions to the mass center, a second group used the gradient directions (orientation) at pixels on, or close to, spiculation to detect and classify spiculated masses [20].…”

Section: Introductionmentioning

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%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

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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“…Rabi Narayan Panda.et.al [24], proposed technique is based on a three-step procedure: regions of interest (ROI) specification, two dimensional wavelet transformation, and feature extraction based on OTSU thresholding the region of interest for the identification of microcalcifications and mass lesions. ROIs are preprocessed using a wavelet-based transformation method and a thresholding technique is applied to exclude microcalcifications and mass lesions.…”

Section: E Existing Research Studymentioning

confidence: 99%

Review on Mammogram Mass Detection by Machine Learning Techniques

Raman¹,

Sumari²,

Then³

et al. 2011

IJCEE

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Abstract-Breast cancer continues to be a significant public health problem in the world and number one cause for death rate in Malaysia. Early detection is the key for improving breast cancer prognosis. Mammography is the most effective tool now available for an early diagnosis of breast cancer. However, the detection of cancer signs in mammograms is a difficult task due to irregular pathological structures and noise which are present in the image. It has been shown that in current breast cancer screenings 8%-20% of the tumors are missed by the radiologists. For this reason, a lot of research is currently being done to develop systems for computer aided detection to improve the accuracy. In this paper, review of mammogram mass detection and segmentation is focused. The main aim of the paper is to summarize and compares the method of mass detection in mammogram images. In specific, preprocessing, segmentation, feature extraction and classifications are discussed, Receiver operating curve and free-response receiver operating curve of each method is highlighted to show the sensitivity and specificity of the tumor detection.

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“…However, for less trained radiologists, experiences come with extensive practices. One way to cope with this dilemma is to use a system to provide them with a second opinion to assist them in improvement upon diagnosis (Kegelmeyer et al, 1994).…”

Section: Introductionmentioning

confidence: 99%

Classification of clustered microcalcifications using a Shape Cognitron neural network

et al. 2003

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Computer-aided mammographic screening for spiculated lesions.

Abstract: The study demonstrated that computer analysis of mammograms can provide a substantial and statistically significant increase in radiologist screening efficacy.

Cited by 267 publications

References 0 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

Review on Mammogram Mass Detection by Machine Learning Techniques

Classification of clustered microcalcifications using a Shape Cognitron neural network

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