Feature analysis methods for intelligent breast imaging parameter optimisation using CMOS active pixel sensors

This thesis explores the concept of real time imaging parameter optimisation in digital mammography using statistical information extracted from the breast during a scan. Transmission and Energy dispersive x-ray diffraction (EDXRD) imaging were the two very different imaging modalities investigated. An attempt to determine if either could be used in a real time imaging system enabling differentiation between healthy and suspicious tissue regions was made. This would consequently enable local regions (potentially cancerous regions) within the breast to be imaged using optimised imaging parameters. The performance of possible statistical feature functions that could be used as information extraction tools were investigated using low exposure breast tissue images. The images were divided into eight regions of interest, seven regions corresponding to suspicious tissue regions marked by a radiologist, where the final region was obtained from a location in the breast consisting solely of healthy tissue. Results obtained from this investigation showed that a minimum of 82% of the suspicious tissue regions were highlighted in all images, whilst the total exposure incident on the sample was reduced in all instances. Three out of the seven (42%) intelligent images resulted in an increased contrast to noise ratio (CNR) compared to the conventionally produced transmission images. Three intelligent images were of similar diagnostic quality to their conventional counter parts whilst one was considerably lower. EDXRD measurements were made on breast tissue samples containing potentially cancerous tissue regions. As the technique is known to be able to distinguish between breast tissue types, diffraction signals were used to produce images corresponding to three suspicious tissue regions consequently enabling pixel intensities within the images to be analysed. A minimum of approximately 70% of the suspicious tissue regions were highlighted in each image, with at least 50% of each image remaining unsuspicious, hence was imaged with a reduced incident exposure

Development of an automated detection algorithm for patient motion blur in digital mammograms

The purpose is to develop and validate an automated method for detecting image unsharpness caused by patient motion blur in digital mammograms. The goal is that such a tool would facilitate immediate re-taking of blurred images, which has the potential to reduce the number of recalled examinations, and to ensure that sharp, high-quality mammograms are presented for reading. To meet this goal, an automated method was developed based on interpretation of the normalized image Wiener Spectrum. A preliminary algorithm was developed using 25 cases acquired using a single vendor system, read by two expert readers identifying the presence of blur, location, and severity. A predictive blur severity score was established using multivariate modeling, which had an adjusted coefficient of determination, R2 =0.63±0.02, for linear regression against the average reader-scored blur severity. A heatmap of the relative blur magnitude showed good correspondence with reader sketches of blur location, with a Spearman rank correlation of 0.70 between the algorithmestimated area fraction with blur and the maximum of the blur area fraction categories of the two readers. Given these promising results, the algorithm-estimated blur severity score and heatmap are proposed to be used to aid observer interpretation. The use of this automated blur analysis approach, ideally with feedback during an exam, could lead to a reduction in repeat appointments for technical reasons, saving time, cost, potential anxiety, and improving image quality for accurate diagnosis.</p

Automated segmentation of radiodense tissue in digitized mammograms using a constrained Neyman-Pearson classifier

Breast cancer is the second leading cause of cancer related mortality among American women. Mammography screening has emerged as a reliable non-invasive technique for early detection of breast cancer. The radiographic appearance of the female breast consists of radiolucent (dark) regions and radiodense (light) regions due to connective and epithelial tissue. It has been established that the percentage of radiodense tissue in a patient\u27s breast can be used as a marker for predicting breast cancer risk. This thesis presents the design, development and validation of a novel automated algorithm for estimating the percentage of radiodense tissue in a digitized mammogram. The technique involves determining a dynamic threshold for segmenting radiodense indications in mammograms. Both the mammographic image and the threshold are modeled as Gaussian random variables and a constrained Neyman-Pearson criteria has been developed for segmenting radiodense tissue. Promising results have been obtained using the proposed technique. Mammograms have been obtained from an existing cohort of women enrolled in the Family Risk Analysis Program at Fox Chase Cancer Center (FCCC). The proposed technique has been validated using a set of ten images with percentages of radiodense tissue, estimated by a trained radiologist using previously established methods. This work is intended to support a concurrent study at the FCCC exploring the association between dietary patterns and breast cancer risk

Pharmacokinetic Analysis of Gd-DTPA Enhancement in dynamic three-dimensional MRI of breast lesions

The purpose of this study was to demonstrate that dynamic MRI covering both breasts can provide sensitivity for tumor detection as well as specificity and sensitivity for differentiation of tumor malignancy. Three-dimensional gradient echo scans were used covering both breasts. Before Gd-DTPA bolus injection, two scans were obtained with different flip angles, and after injection, a dynamic series followed. Thirty-two patients were scanned according to this protocol. From these scans, in addition to enhancement, the value of T1 before injection was obtained. This was used to estimate the concentration of Gd-DTPA as well as the pharmacokinetic parameters governing its time course. Signal enhancement in three-dimensional dynamic scanning was shown to be a sensitive basis for detection of tumors. In our series, all but two mam-mographically suspicious lesions did enhance, and in three cases, additional enhancing lesions were found, two of which were in the contralateral breast. The parameter most suited for classification of breast lesions into benign or malignant was shown to be the pharmacokinetically defined permeability k31, which, for that test, gave a sensitivity of 92% and a specificity of 70%. Our three-dimensional dynamic MRI data are sensitive for detection of mammographically occult breast tumors and specific for classification of these as benign or malignant

Mammography

In this volume, the topics are constructed from a variety of contents: the bases of mammography systems, optimization of screening mammography with reference to evidence-based research, new technologies of image acquisition and its surrounding systems, and case reports with reference to up-to-date multimodality images of breast cancer. Mammography has been lagged in the transition to digital imaging systems because of the necessity of high resolution for diagnosis. However, in the past ten years, technical improvement has resolved the difficulties and boosted new diagnostic systems. We hope that the reader will learn the essentials of mammography and will be forward-looking for the new technologies. We want to express our sincere gratitude and appreciation?to all the co-authors who have contributed their work to this volume

Breast Cancer Detection by Means of Artificial Neural Networks

Breast cancer is a fatal disease causing high mortality in women. Constant efforts are being made for creating more efficient techniques for early and accurate diagnosis. Classical methods require oncologists to examine the breast lesions for detection and classification of various stages of cancer. Such manual attempts are time consuming and inefficient in many cases. Hence, there is a need for efficient methods that diagnoses the cancerous cells without human involvement with high accuracies. In this research, image processing techniques were used to develop imaging biomarkers through mammography analysis and based on artificial intelligence technology aiming to detect breast cancer in early stages to support diagnosis and prioritization of high-risk patients. For automatic classification of breast cancer on mammograms, a generalized regression artificial neural network was trained and tested to separate malignant and benign tumors reaching an accuracy of 95.83%. With the biomarker and trained neural net, a computer-aided diagnosis system is being designed. The results obtained show that generalized regression artificial neural network is a promising and robust system for breast cancer detection. The Laboratorio de Innovacion y Desarrollo Tecnologico en Inteligencia Artificial is seeking collaboration with research groups interested in validating the technology being developed

Breast cancer diagnosis: a survey of pre-processing, segmentation, feature extraction and classification

Machine learning methods have been an interesting method in the field of medical for many years, and they have achieved successful results in various fields of medical science. This paper examines the effects of using machine learning algorithms in the diagnosis and classification of breast cancer from mammography imaging data. Cancer diagnosis is the identification of images as cancer or non-cancer, and this involves image preprocessing, feature extraction, classification, and performance analysis. This article studied 93 different references mentioned in the previous years in the field of processing and tries to find an effective way to diagnose and classify breast cancer. Based on the results of this research, it can be concluded that most of today’s successful methods focus on the use of deep learning methods. Finding a new method requires an overview of existing methods in the field of deep learning methods in order to make a comparison and case study

An Image Processing Framework for Breast Cancer Detection Using Multi-View Mammographic Images

Breast cancer is the leading cause of cancer death in women. The early phase of breast cancer is asymptomatic, without any signs or symptoms. The earlier breast cancer can be detected, the greater chance of cure. Early detection using screening mammography is a common step for detecting the presence of breast cancer. Many studies of computer-based using breast cancer detection have been done previously. However, the detection process for craniocaudal (CC) view and mediolateral oblique (MLO) view angles were done separately. This study aims to improve the detection performance for breast cancer diagnosis with CC and MLO view analysis. An image processing framework for multi-view screening was used to improve the diagnostic results rather than single-view. Image enhancement, segmentation, and feature extraction are all part of the framework provided in this study. The stages of image quality improvement are very important because the contrast of mammographic images is relatively low, so it often overlaps between cancer tissue and normal tissue. Texture-based segmentation utilizing the first-order local entropy approach was used to segment the images. The value of the radius and the region of probable cancer were calculated using the findings of feature extraction. The results of this study show the accuracy of breast cancer detection using CC and MLO views were 88.0% and 80.5% respectively. The proposed framework was useful in the diagnosis of breast cancer, that the detection results and features help clinicians in making treatment