Computer Aided Detection Of Architectural Distortion In Prior Mammograms Of Interval Cancer PDF Download

Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Computer Aided Detection Of Architectural Distortion In Prior Mammograms Of Interval Cancer PDF full book. Access full book title Computer Aided Detection Of Architectural Distortion In Prior Mammograms Of Interval Cancer.

Computer-aided Detection of Architectural Distortion in Prior Mammograms of Interval Cancer

Computer-aided Detection of Architectural Distortion in Prior Mammograms of Interval Cancer
Author: Shantanu Banik
Publisher: Morgan & Claypool Publishers
Total Pages: 195
Release: 2013-01-01
Genre: Technology & Engineering
ISBN: 1627050833
GET BOOK

Download Computer-aided Detection of Architectural Distortion in Prior Mammograms of Interval Cancer Book in PDF, ePub and Kindle

Architectural distortion is an important and early sign of breast cancer, but because of its subtlety, it is a common cause of false-negative findings on screening mammograms. Screening mammograms obtained prior to the detection of cancer could contain subtle signs of early stages of breast cancer, in particular, architectural distortion. This book presents image processing and pattern recognition techniques to detect architectural distortion in prior mammograms of interval-cancer cases. The methods are based upon Gabor filters, phase portrait analysis, procedures for the analysis of the angular spread of power, fractal analysis, Laws' texture energy measures derived from geometrically transformed regions of interest (ROIs), and Haralick's texture features. With Gabor filters and phase-portrait analysis, 4,224 ROIs were automatically obtained from 106 prior mammograms of 56 interval-cancer cases, including 301 true-positive ROIs related to architectural distortion, and from 52 mammograms of 13 normal cases. For each ROI, the fractal dimension, the entropy of the angular spread of power, 10 Laws' texture energy measures, and Haralick's 14 texture features were computed. The areas under the receiver operating characteristic (ROC) curves obtained using the features selected by stepwise logistic regression and the leave-one-image-out method are 0.77 with the Bayesian classifier, 0.76 with Fisher linear discriminant analysis, and 0.79 with a neural network classifier. Free-response ROC analysis indicated sensitivities of 0.80 and 0.90 at 5.7 and 8.8 false positives (FPs) per image, respectively, with the Bayesian classifier and the leave-one-image-out method. The present study has demonstrated the ability to detect early signs of breast cancer 15 months ahead of the time of clinical diagnosis, on the average, for interval-cancer cases, with a sensitivity of 0.8 at 5.7 FP/image. The presented computer-aided detection techniques, dedicated to accurate detection and localization of architectural distortion, could lead to efficient detection of early and subtle signs of breast cancer at pre-mass-formation stages. Table of Contents: Introduction / Detection of Early Signs of Breast Cancer / Detection and Analysis of Oriented Patterns / Detection of Potential Sites of Architectural Distortion / Experimental Set Up and Datasets / Feature Selection and Pattern Classification / Analysis of Oriented Patterns Related to Architectural Distortion / Detection of Architectural Distortion in Prior Mammograms / Concluding Remarks

Analysis of Oriented Texture with application to the Detection of Architectural Distortion in Mammograms

Analysis of Oriented Texture with application to the Detection of Architectural Distortion in Mammograms
Author: Fabio Ayres
Publisher: Springer Nature
Total Pages: 150
Release: 2022-06-01
Genre: Technology & Engineering
ISBN: 3031016475
GET BOOK

Download Analysis of Oriented Texture with application to the Detection of Architectural Distortion in Mammograms Book in PDF, ePub and Kindle

The presence of oriented features in images often conveys important information about the scene or the objects contained; the analysis of oriented patterns is an important task in the general framework of image understanding. As in many other applications of computer vision, the general framework for the understanding of oriented features in images can be divided into low- and high-level analysis. In the context of the study of oriented features, low-level analysis includes the detection of oriented features in images; a measure of the local magnitude and orientation of oriented features over the entire region of analysis in the image is called the orientation field. High-level analysis relates to the discovery of patterns in the orientation field, usually by associating the structure perceived in the orientation field with a geometrical model. This book presents an analysis of several important methods for the detection of oriented features in images, and a discussion of the phase portrait method for high-level analysis of orientation fields. In order to illustrate the concepts developed throughout the book, an application is presented of the phase portrait method to computer-aided detection of architectural distortion in mammograms. Table of Contents: Detection of Oriented Features in Images / Analysis of Oriented Patterns Using Phase Portraits / Optimization Techniques / Detection of Sites of Architectural Distortion in Mammograms

Computerized Analysis of Mammographic Images for Detection and Characterization of Breast Cancer

Computerized Analysis of Mammographic Images for Detection and Characterization of Breast Cancer
Author: Arianna Mencattini
Publisher: Springer Nature
Total Pages: 166
Release: 2022-05-31
Genre: Technology & Engineering
ISBN: 3031016645
GET BOOK

Download Computerized Analysis of Mammographic Images for Detection and Characterization of Breast Cancer Book in PDF, ePub and Kindle

The identification and interpretation of the signs of breast cancer in mammographic images from screening programs can be very difficult due to the subtle and diversified appearance of breast disease. This book presents new image processing and pattern recognition techniques for computer-aided detection and diagnosis of breast cancer in its various forms. The main goals are: (1) the identification of bilateral asymmetry as an early sign of breast disease which is not detectable by other existing approaches; and (2) the detection and classification of masses and regions of architectural distortion, as benign lesions or malignant tumors, in a unified framework that does not require accurate extraction of the contours of the lesions. The innovative aspects of the work include the design and validation of landmarking algorithms, automatic Tabár masking procedures, and various feature descriptors for quantification of similarity and for contour independent classification of mammographic lesions. Characterization of breast tissue patterns is achieved by means of multidirectional Gabor filters. For the classification tasks, pattern recognition strategies, including Fisher linear discriminant analysis, Bayesian classifiers, support vector machines, and neural networks are applied using automatic selection of features and cross-validation techniques. Computer-aided detection of bilateral asymmetry resulted in accuracy up to 0.94, with sensitivity and specificity of 1 and 0.88, respectively. Computer-aided diagnosis of automatically detected lesions provided sensitivity of detection of malignant tumors in the range of [0.70, 0.81] at a range of falsely detected tumors of [0.82, 3.47] per image. The techniques presented in this work are effective in detecting and characterizing various mammographic signs of breast disease.

Analysis of Oriented Texture

Analysis of Oriented Texture
Author: Fabio Ayres
Publisher: Morgan & Claypool
Total Pages: 150
Release: 2011
Genre: Technology & Engineering
ISBN: 9781608450299
GET BOOK

Download Analysis of Oriented Texture Book in PDF, ePub and Kindle

The presence of oriented features in images often conveys important information about the scene or the objects contained; the analysis of oriented patterns is an important task in the general framework of image understanding. As in many other applications of computer vision, the general framework for the understanding of oriented features in images can be divided into low- and high-level analysis. In the context of the study of oriented features, low-level analysis includes the detection of oriented features in images; a measure of the local magnitude and orientation of oriented features over the entire region of analysis in the image is called the orientation field. High-level analysis relates to the discovery of patterns in the orientation field, usually by associating the structure perceived in the orientation field with a geometrical model. This book presents an analysis of several important methods for the detection of oriented features in images, and a discussion of the phase portrait method for high-level analysis of orientation fields. In order to illustrate the concepts developed throughout the book, an application is presented of the phase portrait method to computer-aided detection of architectural distortion in mammograms. Table of Contents: Detection of Oriented Features in Images / Analysis of Oriented Patterns Using Phase Portraits / Optimization Techniques / Detection of Sites of Architectural Distortion in Mammograms

Computer-Aided Interval Change Analysis of Microcalcifications on Mammograms for Breast Cancer Detection

Computer-Aided Interval Change Analysis of Microcalcifications on Mammograms for Breast Cancer Detection
Author:
Publisher:
Total Pages: 27
Release: 2005
Genre:
ISBN:
GET BOOK

Download Computer-Aided Interval Change Analysis of Microcalcifications on Mammograms for Breast Cancer Detection Book in PDF, ePub and Kindle

The goal of this project is to develop a computer-aided diagnosis (CAD) system for automatic interval change analysis of microcalcification clusters on mammograms. Based on our regional registration method a local area on the prior that may contain the corresponding cluster is determined. A search program is used to detect cluster candidates within the local area. The cluster on the current image is then paired with the candidates to form true (TP-TP) or false (TP-FP) pairs. A correspondence classifier (CC) using automatically extracted features is designed to reduce the false pairs. A temporal classifier (TC) based on current and prior information is used if a cluster is detected in the prior, and a current classifier (CurC) based on current information alone is used if no prior cluster is detected. 175 temporal pairs of mammograms were used for evaluation. The search program detected 90.2% of the clusters on the priors with an average of 0.43 FPs/image. The CC identified 85% (l49/175) of the TP-TP pairs with 15 false matches within the 164 image pairs that had detected clusters. The TC achieved a test Az of 0.83 for the 164 pairs for classifying the clusters as malignant or benign. For the 11 clusters without detection on the prior, the test Az by the CurC was 0.72. The radiologist achieved an Az of 0.72 for both the 175 and the 164 temporal pairs.

Computer-Aided Detection of Architectural Distortion in Prior Mammograms of Interval Cancer

Computer-Aided Detection of Architectural Distortion in Prior Mammograms of Interval Cancer
Author: Shantanu Banik
Publisher: Springer Nature
Total Pages: 176
Release: 2022-05-31
Genre: Technology & Engineering
ISBN: 3031016564
GET BOOK

Download Computer-Aided Detection of Architectural Distortion in Prior Mammograms of Interval Cancer Book in PDF, ePub and Kindle

Architectural distortion is an important and early sign of breast cancer, but because of its subtlety, it is a common cause of false-negative findings on screening mammograms. Screening mammograms obtained prior to the detection of cancer could contain subtle signs of early stages of breast cancer, in particular, architectural distortion. This book presents image processing and pattern recognition techniques to detect architectural distortion in prior mammograms of interval-cancer cases. The methods are based upon Gabor filters, phase portrait analysis, procedures for the analysis of the angular spread of power, fractal analysis, Laws' texture energy measures derived from geometrically transformed regions of interest (ROIs), and Haralick's texture features. With Gabor filters and phase-portrait analysis, 4,224 ROIs were automatically obtained from 106 prior mammograms of 56 interval-cancer cases, including 301 true-positive ROIs related to architectural distortion, and from 52 mammograms of 13 normal cases. For each ROI, the fractal dimension, the entropy of the angular spread of power, 10 Laws' texture energy measures, and Haralick's 14 texture features were computed. The areas under the receiver operating characteristic (ROC) curves obtained using the features selected by stepwise logistic regression and the leave-one-image-out method are 0.77 with the Bayesian classifier, 0.76 with Fisher linear discriminant analysis, and 0.79 with a neural network classifier. Free-response ROC analysis indicated sensitivities of 0.80 and 0.90 at 5.7 and 8.8 false positives (FPs) per image, respectively, with the Bayesian classifier and the leave-one-image-out method. The present study has demonstrated the ability to detect early signs of breast cancer 15 months ahead of the time of clinical diagnosis, on the average, for interval-cancer cases, with a sensitivity of 0.8 at 5.7 FP/image. The presented computer-aided detection techniques, dedicated to accurate detection and localization of architectural distortion, could lead to efficient detection of early and subtle signs of breast cancer at pre-mass-formation stages. Table of Contents: Introduction / Detection of Early Signs of Breast Cancer / Detection and Analysis of Oriented Patterns / Detection of Potential Sites of Architectural Distortion / Experimental Set Up and Datasets / Feature Selection and Pattern Classification / Analysis of Oriented Patterns Related to Architectural Distortion / Detection of Architectural Distortion in Prior Mammograms / Concluding Remarks

Development of a Computer-aided Diagnosis System for Early Detection of Masses Using Retrospectively Detected Cancers on Prior Mammograms

Development of a Computer-aided Diagnosis System for Early Detection of Masses Using Retrospectively Detected Cancers on Prior Mammograms
Author:
Publisher:
Total Pages: 128
Release: 2009
Genre:
ISBN:
GET BOOK

Download Development of a Computer-aided Diagnosis System for Early Detection of Masses Using Retrospectively Detected Cancers on Prior Mammograms Book in PDF, ePub and Kindle

The performance of a CAD system for subtle lesions is generally much lower than their performance for less subtle lesions. The goal of this project is to develop a CAD system using advanced computer vision techniques aiming at improved detection of retrospectively seen cancers on prior mammograms and incorporate the developed CAD system into our current CAD system. During the project years, we have performed the following tasks: (1) collect the data sets of digitized film mammograms for training and testing our CAD system, (2) develop a series of single-view computer vision techniques for mass detection and classification in prior mammograms, (3) reduce FPs by correlation of image information from multiple view mammograms of the same patient, (4) develop a information fusion scheme to combine the new CAD system with the existing CAD system for mass detection, and (5) evaluate the effects of the newly developed CAD scheme with a large data set. We have found that our new computer-vision techniques can significantly improve the performance of the CAD system for mass detection by JAFROC analysis. The significance of this project is that the newly developed CAD system may be able to aid radiologists in detecting breast cancers at an early stage. Since early detection and treatment can reduce breast cancer mortality rate and health care costs, the proposed CAD system will improve the efficacy of mammography for breast cancer screening.

Computer-Aided Interval Change Analysis of Microcalifications on Management for Breast Cancer Detection

Computer-Aided Interval Change Analysis of Microcalifications on Management for Breast Cancer Detection
Author:
Publisher:
Total Pages: 33
Release: 2006
Genre:
ISBN:
GET BOOK

Download Computer-Aided Interval Change Analysis of Microcalifications on Management for Breast Cancer Detection Book in PDF, ePub and Kindle

The goal of this project is to develop a computer-aided diagnosis (CAD) system for automatic interval change analysis of microcalcification clusters on mammograms. Based on our regional registration method and a search program cluster candidates were detected within the local area on the prior. The cluster on the current image is then paired with the candi-dates to form true (TP-TP) or false (TP-FP) pairs and a correspondence classifier is designed to reduce the (TP-FP). A temporal classifier (TC) based on current and prior information is used if a cluster is detected in the prior, and a current classifier (CurC) based on current information alone is used if no prior cluster is detected. For the TC an LDA, SVM and NN were used. 175 temporal pairs of mammograms were used for evaluation. The registration stage identified 85% (1491175) of the TP-TP pairs with 15 false matches within the 164 image pairs that had detected clusters. The TC based on LDA, SVM and NN achieved a test Az of 0.83, 0.82, 0.84, respectively, for the 164 pairs for classifying the clusters as malignant or benign. For the II clusters without detection on the prior, the test Az by the CurC was 0.72. Four radiologists participated in pilot observer study using our CAD. The average Az in estimating the likelihood of malignancy was 0.70 without CAD and improved to 0.77 with CAD(p=O.04).