Journal ArticleDOI
Statistical textural features for detection of microcalcifications in digitized mammograms
Jong-Kook Kim,HyunWook Park +1 more
TLDR
The surrounding region-dependence method is shown to be superior to the conventional texture-analysis methods with respect to classification accuracy and computational complexity.Abstract:
Clustered microcalcifications on X-ray mammograms are an important sign for early detection of breast cancer. Texture-analysis methods can be applied to detect clustered microcalcifications in digitized mammograms. In this paper, a comparative study of texture-analysis methods is performed for the surrounding region-dependence method, which has been proposed by the authors, and conventional texture-analysis methods, such as the spatial gray level dependence method, the gray-level run-length method, and the gray-level difference method. Textural features extracted by these methods are exploited to classify regions of interest (ROI's) into positive ROI's containing clustered microcalcifications and negative ROI's containing normal tissues. A three-layer backpropagation neural network is used as a classifier. The results of the neural network for the texture-analysis methods are evaluated by using a receiver operating-characteristics (ROC) analysis. The surrounding region-dependence method is shown to be superior to the conventional texture-analysis methods with respect to classification accuracy and computational complexity.read more
Citations
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Journal ArticleDOI
Computer-aided detection and classification of microcalcifications in mammograms: a survey
TL;DR: The high correlation between the appearance of the microcalcification clusters and the diseases show that the CAD (computer aided diagnosis) systems for automated detection/classification of MCCs will be very useful and helpful for breast cancer control.
Patent
Computer-aided image analysis
TL;DR: In this paper, digitized image data are input into a plurality of subsystems, each subsystem having one or more support vector machines, and each subsystem analyzes the data relevant to a different feature or characteristic found within the image, then the output for all subsystems is input into an overall support vector machine analyzer which combines the data to make a diagnosis, decision or other action which utilizes the knowledge obtained from the image.
Journal ArticleDOI
Detection of prostate cancer by integration of line-scan diffusion, T2-mapping and T2-weighted magnetic resonance imaging; a multichannel statistical classifier
Ian Chan,Ian Chan,William M. Wells,William M. Wells,Robert V. Mulkern,Steven Haker,Jianqing Zhang,Kelly H. Zou,Kelly H. Zou,Stephan E. Maier,Clare M. Tempany +10 more
TL;DR: By integrating the information from multiple images and capturing the textural and anatomical features in tumor areas, summary statistical maps can potentially aid in image-guided prostate biopsy and assist in guiding and controlling delivery of localized therapy under image guidance.
Journal ArticleDOI
Breast cancer diagnosis using self-organizing map for sonography.
TL;DR: This computer-aided diagnosis system can provide a useful tool and its high negative predictive value could potentially help avert benign biopsies.
References
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Journal ArticleDOI
Textural Features for Image Classification
TL;DR: These results indicate that the easily computable textural features based on gray-tone spatial dependancies probably have a general applicability for a wide variety of image-classification applications.
Journal ArticleDOI
Texture analysis using gray level run lengths
TL;DR: In this paper, a set of texture features based on gray level run lengths is described, and good classification results are obtained with these features on a sets of samples representing nine terrain types.
Journal ArticleDOI
ROC methodology in radiologic imaging
TL;DR: This article develops ROC concepts in an intuitive way by identifying the fundamental issues that motivate ROC analysis and practical techniques for ROC data collection and data analysis.
Texture analysis using grey level run lengths
TL;DR: A set of texture features based on gray level run lengths is described, and good classification results are obtained with these features on a set of samples representing nine terrain types.