Detection of microcalcifications in mammograms using local maxima andadaptive wavelet transform analysis

A method for computer- aided diagnosis of micro calcification clusters in mammograms images presented . Micro calcification clus.eni which are an early sign of bread cancer appear as isolated bright spots in mammograms. Therefore they correspond to local maxima of the image. The local maxima of the image is lint detected and they are ranked according to it higher-order statistical test performed over the sub band domain data

Fractal based techniques for classification of mammograms and identification of microcalcifications

After skin cancer, breast cancer accounts for the second greatest number of cancer diagnoses in women. Currently the etiologies of breast cancer are unknown, and there is no generally accepted therapy for preventing it. Therefore, the best way to improve the prognosis for breast cancer is early detection and treatment. Computer aided detection systems (CAD) for detecting masses or micro-calcifications in mammograms have already been used and proven to be a potentially powerful tool , so the radiologists are attracted by the effectiveness of clinical application of CAD systems. Fractal geometry is well suited for describing the complex physiological structures that defy the traditional Euclidean geometry, which is based on smooth shapes. The major contribution of this research include the development of • A new fractal feature to accurately classify mammograms into normal and normal (i)With masses (benign or malignant) (ii) with microcalcifications (benign or malignant) • A novel fast fractal modeling method to identify the presence of microcalcifications by fractal modeling of mammograms and then subtracting the modeled image from the original mammogram. The performances of these methods were evaluated using different standard statistical analysis methods. The results obtained indicate that the developed methods are highly beneficial for assisting radiologists in making diagnostic decisions. The mammograms for the study were obtained from the two online databases namely, MIAS (Mammographic Image Analysis Society) and DDSM (Digital Database for Screening Mammography.

A New Fast Fractal Modeling Approach for the Detection of Microcalcifications in Mammograms

In this paper, a novel fast method for modeling mammograms by deterministic fractal coding approach to detect the presence of microcalcifications, which are early signs of breast cancer, is presented. The modeled mammogram obtained using fractal encoding method is visually similar to the original image containing microcalcifications, and therefore, when it is taken out from the original mammogram, the presence of microcalcifications can be enhanced. The limitation of fractal image modeling is the tremendous time required for encoding. In the present work, instead of searching for a matching domain in the entire domain pool of the image, three methods based on mean and variance, dynamic range of the image blocks, and mass center features are used. This reduced the encoding time by a factor of 3, 89, and 13, respectively, in the three methods with respect to the conventional fractal image coding method with quad tree partitioning. The mammograms obtained from The Mammographic Image Analysis Society database (ground truth available) gave a total detection score of 87.6%, 87.6%, 90.5%, and 87.6%, for the conventional and the proposed three methods, respectively.

Development of a CAD system for automatic classification of microcalcifications based on FPGA

This paper presents a Computer Aided Diagnosis (CAD) system that automatically classifies microcalcifications detected on digital mammograms into one of the five types proposed by Michele Le Gal, a classification scheme that allows radiologists to determine whether a breast tumor is malignant or not without the need for surgeries. The developed system uses a combination of wavelets and Artificial Neural Networks (ANN) and is executed on an Altera DE2-115 Development Kit, a kit containing a Field-Programmable Gate Array (FPGA) that allows the system to be smaller, cheaper and more energy efficient. Results have shown that the system was able to correctly classify 96.67% of test samples, which can be used as a second opinion by radiologists in breast cancer early diagnosis. (C) 2013 The Authors. Published by Elsevier B.V.

Immunodetection of bone-related proteins in microcalcifications of human dental pulp

Objective: Root canal obliterations may pose esthetic and clinical problems or may even be a risk factor for tooth survival. Microcalcifications in the pulp can be so extensive that the entire root canal system becomes obliterated. Since bone sialoprotein (BSP) and osteopontin (OPN) are involved in both physiological and pathological mineralization processes, our hypothesis was that these two bone-related noncollagenous proteins are present in microcalcifications of the pulp. The purpose of this study was, therefore, to characterize the nature of microcalcifications in the pulp of aged human teeth. Methods: From a large collection of human teeth, 10 were found to exhibit pulpal microcalcifications. The teeth were extracted for periodontal reasons from 39-60 year old patients. After fixation in aldehydes and decalcification, teeth were processed for embedding in LR White resin for analysis in the light and transmission electron microscope. For the detection of BSP and OPN, post-embedding high resolution immunocytochemistry was applied. Results: The microcalcifications were round or elongated, occasionally coalescing, and intensely stained with toluidine blue. Collagen fibrils were found in most but not all microcalcifications. All microcalcifications were immunoreactive for both antibodies and showed an identical labeling pattern. Gold particle labeling was extensively found throughout the interfibrillar ground substance of the microcalcifications, whereas the dentin matrix lacked immunolabeling. Conclusion: BSP and OPN appear to be major matrix constituents of pulp microcalcifications and may thus, like in other mineralized tissues, be involved in their mineralization process.

Improvement for detection of microcalcifications through clustering algorithms and artificial neural networks

A new method for detecting microcalcifications in regions of interest (ROIs) extracted from digitized mammograms is proposed. The top-hat transform is a technique based on mathematical morphology operations and, in this paper, is used to perform contrast enhancement of the mi-crocalcifications. To improve microcalcification detection, a novel image sub-segmentation approach based on the possibilistic fuzzy c-means algorithm is used. From the original ROIs, window-based features, such as the mean and standard deviation, were extracted; these features were used as an input vector in a classifier. The classifier is based on an artificial neural network to identify patterns belonging to microcalcifications and healthy tissue. Our results show that the proposed method is a good alternative for automatically detecting microcalcifications, because this stage is an important part of early breast cancer detection

Microcalcifications Detection using PFCM and ANN

This work presents a method to detect Microcalcifications in Regions of Interest from digitized mammograms. The method is based mainly on the combination of Image Processing, Pattern Recognition and Artificial Intelligence. The Top-Hat transform is a technique based on mathematical morphology operations that, in this work is used to perform contrast enhancement of microcalcifications in the region of interest. In order to find more or less homogeneous regions in the image, we apply a novel image sub-segmentation technique based on Possibilistic Fuzzy c-Means clustering algorithm. From the original region of interest we extract two window-based features, Mean and Deviation Standard, which will be used in a classifier based on a Artificial Neural Network in order to identify microcalcifications. Our results show that the proposed method is a good alternative in the stage of microcalcifications detection, because this stage is an important part of the early Breast Cancer detection

Bone sialoprotein supports breast cancer cell adhesion proliferation and migration through differential usage of the αvβ3 and αvβ5 integrins

Bone sialoprotein (BSP), a secreted glycoprotein found in bone matrix, has been implicated in the formation of mammary microcalcifications and osteotropic metastasis of human breast cancer (HBC). BSP possesses an integrin-binding RGD (Arg-Gly-Asp) domain, which may promote interactions between HBC cells and bone extracellular matrix. Purified BSP, recombinant human BSP fragments and BSP-derived RGD peptides are shown to elicit migratory, adhesive, and proliferative responses in the MDA-MB-231 HBC cell line. Recombinant BSP fragment analysis localized a significant component of these activities to the RGD domain of the protein, and synthetic RGD peptides with BSP flanking sequences (BSPRGD) also conferred these responses. The fibronectin-derived RGD counterpart, GRGDSP (Gly-Arg-Gly-Asp-Ser-Pro), could not support these cellular responses, emphasizing specificity of the BSP configuration. Although most of the proliferative and adhesive responses could be attributed to RGD interactions, these interactions were only partly responsible for the migrational responses. Experiments with integrin-blocking antibodies demonstrated that BSP-RGD-induced migration utilizes the αvβ3 vitronectin receptor, whereas adhesion and proliferation responses were αvβ5-mediated. Using fluorescence activated cell sorting, we selected two separate subpopulations of MDA-MB-231 cells enriched for αvβ3 or αvβ5 respectively. Although some expression of the alternate αv integrin was still retained, the αvβ5-enriched MDA-MB-231 cells showed enhanced proliferative and adhesive responses, whereas the αvβ3-enriched subpopulation was suppressed for proliferation and adhesion, but showed enhanced migratory responses to BSP-RGD. In addition, similar analysis of two other HBC cell lines showed less marked, but similar RGD-dependent trends in adhesion and proliferation to the BSP fragments. Collectively, these data demonstrate BSP effects on proliferative, migratory, and adhesive functions in HBC cells and that the RGD-mediated component differentially employs αvβ3 and αvβ5 integrin receptors.

How does calcification influence plaque vulnerability? Insights from fatigue analysis

Background Calcification is commonly believed to be associated with cardiovascular disease burden. But whether or not the calcifications have a negative effect on plaque vulnerability is still under debate. Methods and Results Fatigue rupture analysis and the fatigue life were used to evaluate the rupture risk. An idealized baseline model containing no calcification was first built. Based on the baseline model, we investigated the influence of calcification on rupture path and fatigue life by adding a circular calcification and changing its location within the fibrous cap area. Results show that 84.0% of calcified cases increase the fatigue life up to 11.4%. For rupture paths 10D far from the calcification, the life change is negligible. Calcifications close to lumen increase more fatigue life than those close to the lipid pool. Also, calcifications in the middle area of fibrous cap increase more fatigue life than those in the shoulder area. Conclusion Calcifications may play a positive role in the plaque stability. The influence of the calcification only exists in a local area. Calcifications close to lumen may be influenced more than those close to lipid pool. And calcifications in the middle area of fibrous cap are seemly influenced more than those in the shoulder area.

Imagerie par susceptibilité magnétique appliquée aux seins

Le manuscrit suivant porte sur le développement d’une méthodologie de cartographie de la susceptibilité magnétique. Cette méthodologie a été appliquée au niveau des seins à des fins de détection de microcalcifications. Afin de valider ces algorithmes, un fantôme numérique ainsi qu’un fantôme réel ont été créés. À l’aide de ces images, les paramètres modifiables de notre méthodologie ont été ajustés. Par la suite, les problèmes reliés à l’imagerie du sein ont été explorés, tel la présence de gras ainsi que la proximité des poumons. Finalement, des images in vivo, acquises à 1.5 et 7.0 Tesla ont été analysées par notre méthodologie. Sur ces images 1.5T, nous avons réussi à observer la présence de microcalcifications. D’un autre côté, les images 7.0T nous ont permis de présenter un meilleur contraste que les images standards de magnitude.

Enhancement of Calcifications in Mammograms using Volterra Series based Quadratic Filter

The paper summarizes the design and implementation of a quadratic edge detection filter, based on Volterra series, for enhancing calcifications in mammograms. The proposed filter can account for much of the polynomial nonlinearities inherent in the input mammogram image and can replace the conventional edge detectors like Laplacian, gaussian etc. The filter gives rise to improved visualization and early detection of microcalcifications, which if left undetected, can lead to breast cancer. The performance of the filter is analyzed and found superior to conventional spatial edge detectors

Classification of Mammograms and DW T based Detection of Microcalcification

Cancer treatment is most effective when it is detected early and the progress in treatment will be closely related to the ability to reduce the proportion of misses in the cancer detection task. The effectiveness of algorithms for detecting cancers can be greatly increased if these algorithms work synergistically with those for characterizing normal mammograms. This research work combines computerized image analysis techniques and neural networks to separate out some fraction of the normal mammograms with extremely high reliability, based on normal tissue identification and removal. The presence of clustered microcalcifications is one of the most important and sometimes the only sign of cancer on a mammogram. 60% to 70% of non-palpable breast carcinoma demonstrates microcalcifications on mammograms [44], [45], [46].WT based techniques are applied on the remaining mammograms, those are obviously abnormal, to detect possible microcalcifications. The goal of this work is to improve the detection performance and throughput of screening-mammography, thus providing a ‘second opinion ‘ to the radiologists. The state-of- the- art DWT computation algorithms are not suitable for practical applications with memory and delay constraints, as it is not a block transfonn. Hence in this work, the development of a Block DWT (BDWT) computational structure having low processing memory requirement has also been taken up.

Fast Fractal Coding Method for the Detection of Microcalcification in Mammograms

The presence of microcalcifications in mammograms can be considered as an early indication of breast cancer. A fastfractal block coding method to model the mammograms fordetecting the presence of microcalcifications is presented in this paper. The conventional fractal image coding method takes enormous amount of time during the fractal block encoding.procedure. In the proposed method, the image is divided intoshade and non shade blocks based on the dynamic range, andonly non shade blocks are encoded using the fractal encodingtechnique. Since the number of image blocks is considerablyreduced in the matching domain search pool, a saving of97.996% of the encoding time is obtained as compared to theconventional fractal coding method, for modeling mammograms.The above developed mammograms are used for detectingmicrocalcifications and a diagnostic efficiency of 85.7% isobtained for the 28 mammograms used.

Microcalcification evaluation in computer assisted diagnosis for digital mammography

In order to develop applications for z;isual interpretation of medical images, the early detection and evaluation of microcalcifications in digital mammograms is verg important since their presence is often associated with a high incidence of breast cancers. Accurate classification into benign and malignant groups would help improve diagnostic sensitivity as well as reduce the number of unnecessa y biopsies. The challenge here is the selection of the useful features to distinguish benign from malignant micro calcifications. Our purpose in this work is to analyse a microcalcification evaluation method based on a set of shapebased features extracted from the digitised mammography. The segmentation of the microcalcifications is performed using a fixed-tolerance region growing method to extract boundaries of calcifications with manually selected seed pixels. Taking into account that shapes and sizes of clustered microcalcifications have been associated with a high risk of carcinoma based on digerent subjective measures, such as whether or not the calcifications are irregular, linear, vermiform, branched, rounded or ring like, our efforts were addressed to obtain a feature set related to the shape. The identification of the pammeters concerning the malignant character of the microcalcifications was performed on a set of 146 mammograms with their real diagnosis known in advance from biopsies. This allowed identifying the following shape-based parameters as the relevant ones: Number of clusters, Number of holes, Area, Feret elongation, Roughness, and Elongation. Further experiments on a set of 70 new mammogmms showed that the performance of the classification scheme is close to the mean performance of three expert radiologists, which allows to consider the proposed method for assisting the diagnosis and encourages to continue the investigation in the sense of adding new features not only related to the shape