Journal ArticleDOI
An Automatic Mass Detection System in Mammograms Based on Complex Texture Features
TLDR
This study presents an automatic CADe system that uses local and discrete texture features for mammographic mass detection and proposes two complex feature extraction methods based on cooccurrence matrix and optical density transformation to describe local texture characteristics and the discrete photometric distribution of each ROI.Abstract:
It is difficult for radiologists to identify the masses on a mammogram because they are surrounded by complicated tissues. In current breast cancer screening, radiologists often miss approximately 10-30% of tumors because of the ambiguous margins of lesions and visual fatigue resulting from long-time diagnosis. For these reasons, many computer-aided detection (CADe) systems have been developed to aid radiologists in detecting mammographic lesions which may indicate the presence of breast cancer. This study presents an automatic CADe system that uses local and discrete texture features for mammographic mass detection. This system segments some adaptive square regions of interest (ROIs) for suspicious areas. This study also proposes two complex feature extraction methods based on cooccurrence matrix and optical density transformation to describe local texture characteristics and the discrete photometric distribution of each ROI. Finally, this study uses stepwise linear discriminant analysis to classify abnormal regions by selecting and rating the individual performance of each feature. Results show that the proposed system achieves satisfactory detection performance.read more
Citations
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Journal ArticleDOI
Computer-Aided Diagnosis of Mammographic Masses Using Scalable Image Retrieval
TL;DR: A scalable method for retrieval and diagnosis of mammographic masses, where scale-invariant feature transform (SIFT) features are extracted and searched in a vocabulary tree, which stores all the quantized features of previously diagnosed mammographic ROIs.
Journal ArticleDOI
Improved Threshold Based and Trainable Fully Automated Segmentation for Breast Cancer Boundary and Pectoral Muscle in Mammogram Images
Dilovan Asaad Zebari,Diyar Qader Zeebaree,Adnan Mohsin Abdulazeez,Habibollah Haron,Haza Nuzly Abdull Hamed +4 more
TL;DR: The overall ROI performance of the proposed method showed improving accuracy after improving the threshold technique for background segmentation and building an ML technique for pectoral muscle segmentation.
Journal ArticleDOI
LBP operators on curvelet coefficients as an algorithm to describe texture in breast cancer tissues
Daniel O. Tambasco Bruno,Marcelo Zanchetta do Nascimento,Rodrigo Pereira Ramos,Valério Ramos Batista,Leandro Alves Neves,Alessandro Santana Martins +5 more
TL;DR: The association of curvelet transform, local binary pattern and ANOVA with the PL classifier achieved higher AUC and AC values for all cases and may contribute to the diagnosis of breast tissues (mammographic and histopathological images).
Journal ArticleDOI
A dual stage adaptive thresholding (DuSAT) for automatic mass detection in mammograms
TL;DR: A new computer aided approach to detect the abnormalities in the digital mammograms using a Dual Stage Adaptive Thresholding (DuSAT) method that helps the radiologists in diagnosis of breast cancer at early stage is investigated.
Journal ArticleDOI
Comparative evaluation of autocontouring in clinical practice: A practical method using the Turing test.
Mark Gooding,Annamarie J. Smith,Maira Tariq,Paul Aljabar,Devis Peressutti,Judith van der Stoep,Bart Reymen,Daisy Emans,D. Hattu,Judith van Loon,Maud de Rooy,Rinus Wanders,Stéphanie Peeters,Tim Lustberg,Johan van Soest,Andre Dekker,Wouter van Elmpt +16 more
TL;DR: The inability to accurately judge the source of a contour indicates a reduced need for editing and therefore a greater time saving overall, and task-based assessments of contouring performance may be considered as an additional way of evaluating the clinical utility of autosegmentation methods.
References
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Journal ArticleDOI
Statistical and structural approaches to texture
TL;DR: This survey reviews the image processing literature on the various approaches and models investigators have used for texture, including statistical approaches of autocorrelation function, optical transforms, digital transforms, textural edgeness, structural element, gray tone cooccurrence, run lengths, and autoregressive models.
Book
Handbook of Medical Imaging, Volume 2. Medical Image Processing and Analysis
TL;DR: Wasting The Book Of MaladiesBeyond Midi The Handbook Of Musical CodesAliens Ufos And Unexplained Encounters Paranormal Investigations2012 Harley Softail Service ManualWood Design Design BooksVehicular Communications And Networks Architectures Protocols Operation And Deployment Woodhead Publishing Series In Electronic And Optical MaterialsX Teams How To Build Teams That Lead Innovate And Succeed 1st EditionCataloging And Classification An IntroductionWave Motion Physics Class 12 Th Notes
Journal ArticleDOI
Computer-aided classification of mammographic masses and normal tissue: linear discriminant analysis in texture feature space
Heang Ping Chan,Datong Wei,Mark A. Helvie,Berkman Sahiner,Dorit D. Adler,Mitchell M. Goodsitt,Nicholas Petrick +6 more
TL;DR: The results demonstrate the feasibility of using linear discriminant analysis in the texture feature space for classification of true and false detections of masses on mammograms in a computer-aided diagnosis scheme.
Journal ArticleDOI
Automatic identification of the pectoral muscle in mammograms
TL;DR: A new method is proposed for the identification of the pectoral muscle in MLO mammograms based upon a multiresolution technique using Gabor wavelets, which overcomes the limitation of the straight-line representation considered in the initial investigation using the Hough transform.