An adaptive density-weighted contrast enhancement filter for mammographic breast mass detection

Petrick, Nicholas; Chan, Heang Ping; Sahiner, Berkman; Wei, Datong

doi:10.1109/42.481441

Cited by 190 publications

(98 citation statements)

References 20 publications

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“…Fo r instance, at a sp ecificity of 1 FP/im age a true-posi tive fraction of only 30 % is reported by N ishikaw a et al [24] fo r masses w ith diam eters between 8 and 14 mm. S im ila r results w ere obtained by P e trick et al [26] for masses o f unknown size. F o r stellate lesions and m icro calcificatio n clusters the sensitivity is approxi m ately 9 0 % at the same specificity.…”

Section: Resultsmentioning

confidence: 85%

“…O n a test set of 12 images showing an irregular mass their method co rrectly labeled the mass in 8 cases, but no false-positive rate was reported. F in a lly , in a recent study P e trick et al [26] suggest the use of a new contrast enhancement filte r fo llo w ed by edge detection to gener ate target signals, w hich are then classified by a statisti cal or neural netw ork classifier.…”

Section: Detection Of Massesmentioning

confidence: 99%

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Computer-assisted reading of mammograms

Karssemeijer

Hendriks

1997

Eur. Radiol.

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Abstract. Techniques developed in com puter visionand automated pattern recognition can be applied to assist radiologists in reading mammograms. W ith the introduction of direct digital mammography this w ill become a feasible approach. A radiologist in breast cancer screening can use findings of the com puter as a second opinion, or as a pointer to suspicious re gions. This may increase the sensitivity and specificity of screening programs, and it may avoid the need for double reading. In this paper methods which have been developed fo r automated detection of mammo graphie abnorm alities are reviewed. Program s fo r de tecting m icrocalcification clusters and stellate lesions have reached a le ve l of performance which makes app lication in practice viable. Current programs fo r rec ognition of masses and asymmetry perform less well. Large-scale studies still have to demonstrate if rad io l ogists in a screening situation can deal w ith the re la tiv e ly large num ber of false positives w hich are m arked by com puter programs, where the num ber of norm al cases is much higher than in observer experi ments conducted thus far,

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Section: Resultsmentioning

confidence: 85%

Section: Detection Of Massesmentioning

confidence: 99%

Computer-assisted reading of mammograms

Karssemeijer

Hendriks

1997

Eur. Radiol.

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“…The main purpose of circularity is to show the circular degree of masses [25]. The higher the circularity is, the more circular the object tends to be.…”

Section: Circularitymentioning

confidence: 99%

A Computer-Aided System for Mass Detection and Classification in Digitized Mammograms

Yang

Wang

Chung

et al. 2005

Biomed. Eng. Appl. Basis Commun.

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This paper presents a computer-assisted diagnostic system for mass detection and classification, which performs mass detection on regions of interest followed by the benign-malignant classification on detected masses. In order for mass detection to be effective, a sequence of preprocessing steps are designed to enhance the intensity of a region of interest, remove the noise effects and locate suspicious masses using five texture features generated from the spatial gray level difference matrix (SGLDM) and fractal dimension. Finally, a probabilistic neural network (PNN) coupled with entropic thresholding techniques is developed for mass extraction. Since the shapes of masses are crucial in classification between benignancy and malignancy, four shape features are further generated and joined with the five features previously used in mass detection to be implemented in another PNN for mass classification. To evaluate our designed system a data set collected in the Taichung Veteran General Hospital, Taiwan, R.O.C. was used for performance evaluation. The results are encouraging and have shown promise of our system.

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“…Tested on a small database, a difference in the training and testing results was found. Petrick et al [21] obtained potential masses using an adaptive density-weighted contrast enhancement (DWCE) filter and LaplacianGaussian (LG) edge detection. Then morphological features were extracted and input to a classifier to differentiate normal ROIs and mass ROIs.…”

Section: Mass Detectionmentioning

confidence: 99%

A Survey of Computer-aided Detection of Breast Cancer with Mammography

Yanfeng¹,

Chen²,

Chen³

et al. 2016

J Health Med Inform

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Computer-aided detection (CAD) systems can be served as a second view for radiologist. An overview of recent development in CAD methods are presented in this paper. Abnormalities detection, abnormalities classification and content-based image retrieval (CBIR) are briefly reviewed. For the abnormalities detection, micro-calcification detection, mass detection and multi-view based detection are introduced. For the abnormalities classification, micro-calcification classification and mass classification are given.

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An adaptive density-weighted contrast enhancement filter for mammographic breast mass detection

Cited by 190 publications

References 20 publications

Computer-assisted reading of mammograms

Computer-assisted reading of mammograms

A Computer-Aided System for Mass Detection and Classification in Digitized Mammograms

A Survey of Computer-aided Detection of Breast Cancer with Mammography

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