Fuzzy technique for microcalcifications clustering in digital mammograms

Background Mammography has established itself as the most efficient technique for the identification of the pathological breast lesions. Among the various types of lesions, microcalcifications are the most difficult to identify since they are quite small (0.1-1.0 mm) and often poorly contrasted against an images background. Within this context, the Computer Aided Detection (CAD) systems could turn out to be very useful in breast cancer control. Methods In this paper we present a potentially powerful microcalcifications cluster enhancement method applicable to digital mammograms. The segmentation phase employs a form filter, obtained from LoG filter, to overcome the dependence from target dimensions and to optimize the recognition efficiency. A clustering method, based on a Fuzzy C-means (FCM), has been developed. The described method, Fuzzy C-means with Features (FCM-WF), was tested on simulated clusters of microcalcifications, implying that the location of the cluster within the breast and the exact number of microcalcifications are known.The proposed method has been also tested on a set of images from the mini-Mammographic database provided by Mammographic Image Analysis Society (MIAS) publicly available. Results The comparison between FCM-WF and standard FCM algorithms, applied on both databases, shows that the former produces better microcalcifications associations for clustering than the latter: with respect to the private and the public database we had a performance improvement of 10% and 5% with regard to the Merit Figure and a 22% and a 10% of reduction of false positives potentially identified in the images, both to the benefit of the FCM-WF. The method was also evaluated in terms of Sensitivity (93% and 82%), Accuracy (95% and 94%), FP/image (4% for both database) and Precision (62% and 65%). Conclusions Thanks to the private database and to the informations contained in it regarding every single microcalcification, we tested the developed clustering method with great accuracy. In particular we verified that 70% of the injected clusters of the private database remained unaffected if the reconstruction is performed with the FCM-WF. Testing the method on the MIAS databases allowed also to verify the segmentation properties of the algorithm, showing that 80% of pathological clusters remained unaffected

Segmentation and Feature Extraction of Tumors from Digital Mammograms

Mammography is one of the available techniques for the early detection of masses or abnormalities which is related to breast cancer. Breast Cancer is the uncontrolled of cells in the breast region, which may affect the other parts of the body. The most common abnormalities that might indicate breast cancer are masses and calcifications. Masses appear in a mammogram as fine, granular clusters and also masses will not have sharp boundaries, so often difficult to identify in a raw mammogram. Digital Mammography is one of the best available technologies currently being used for the early detection of breast cancer. Computer Aided Detection System has to be developed for the detection of masses and calcifications in Digital Mammogram, which acts as a secondary tool for the radiologists for diagnosing the breast cancer. In this paper, we have proposed a secondary tool for the radiologists that help them in the segmentation and feature extraction process. Keywords: Mammography, Breast Cancer, Masses, Calcification, Digital Mammography, Computer Aided Detection System, Segmentation, Feature Extractio

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

Designing a secure ubiquitous mammography consultation system

This thesis attempts to design and develop a prototype for mammography image consultation that can work securely within a ubiquitous environment. Mammogram images differ largely from other type of images and it requires special and dedicated techniques to identify the required regions of interest. Thus in Chapter 2 we started to explore the affectivity of the various traditional techniques based on convolution operators (e.g. Sobol, Pretwitt, Canny) for mammography edge detection. The second part of chapter 2 tries to enhance the results obtained via the traditional techniques by hybriding some of them. The hybriding technique is called in our thesis as Pipelined Operators. In this direction we proposed four pipeline operators, which contribute to the edge enhancement as well as abnormalities rendering through the introduction of an additional coloring mechanism. Although the visualization pipelines represent in our view an advancement on the traditional techniques applied to mammograms, such pipelines expose healthcare users to further usage complexities. For this purpose we extended our research work in chapter 2 to find a better single technique that can work smoothly within the healthcare system. In this direction, we developed in the third part of chapter 2 a novel technique for finding edges based on analyzing the dynamic and fuzzy nature of edges in mammograms. We called our developed method as "Dynamic Fuzzy Classifier or the DFC"

Nonlinear Parametric and Neural Network Modelling for Medical Image Classification

System identification and artificial neural networks (ANN) are families of algorithms used in systems engineering and machine learning respectively that use structure detection and learning strategies to build models of complex systems by taking advantage of input-output type data. These models play an essential role in science and engineering because they fill the gap in those cases where we know the input-output behaviour of a system, but there is not a mathematical model to understand and predict its changes in future or even prevent threats. In this context, the nonlinear approximation of systems is nowadays very popular since it better describes complex instances. On the other hand, digital image processing is an area of systems engineering that is expanding the analysis dimension level in a variety of real-life problems while it is becoming more attractive and affordable over time. Medicine has made the most of it by supporting important human decision-making processes through computer-aided diagnosis (CAD) systems. This thesis presents three different frameworks for breast cancer detection, with approaches ranging from nonlinear system identification, nonlinear system identification coupled with simple neural networks, to multilayer neural networks. In particular, the nonlinear system identification approaches termed the Nonlinear AutoRegressive with eXogenous inputs (NARX) model and the MultiScales Radial Basis Function (MSRBF) neural networks appear for the first time in image processing. Along with the above contributions takes place the presentation of the Multilayer-Fuzzy Extreme Learning Machine (ML-FELM) neural network for faster training and more accurate image classification. A central research aim is to take advantage of nonlinear system identification and multilayer neural networks to enhance the feature extraction process, while the classification in CAD systems is bolstered. In the case of multilayer neural networks, the extraction is carried throughout stacked autoencoders, a bottleneck network architecture that promotes a data transformation between layers. In the case of nonlinear system identification, the goal is to add flexible models capable of capturing distinctive features from digital images that might be shortly recognised by simpler approaches. The purpose of detecting nonlinearities in digital images is complementary to that of linear models since the goal is to extract features in greater depth, in which both linear and nonlinear elements can be captured. This aim is relevant because, accordingly to previous work cited in the first chapter, not all spatial relationships existing in digital images can be explained appropriately with linear dependencies. Experimental results show that the methodologies based on system identification produced reliable images models with customised mathematical structure. The models came to include nonlinearities in different proportions, depending upon the case under examination. The information about nonlinearity and model structure was used as part of the whole image model. It was found that, in some instances, the models from different clinical classes in the breast cancer detection problem presented a particular structure. For example, NARX models of the malignant class showed higher non-linearity percentage and depended more on exogenous inputs compared to other classes. Regarding classification performance, comparisons of the three new CAD systems with existing methods had variable results. As for the NARX model, its performance was superior in three cases but was overcame in two. However, the comparison must be taken with caution since different databases were used. The MSRBF model was better in 5 out of 6 cases and had superior specificity in all instances, overcoming in 3.5% the closest model in this line. The ML-FELM model was the best in 6 out of 6 cases, although it was defeated in accuracy by 0.6% in one case and specificity in 0.22% in another one