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 oftenassociated 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 microcalcificationsis 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 microcalcificationshave 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 senseof adding new features not only related to the shape

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

Microcalcification Border Characterization Using Wavelets on Digital Mammograms

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Microcalcification enhancement and classification on mammograms using the wavelet transform

This paper presents a method to enhance microcalcifications and classify their borders by applying the wavelet transform. Decomposing an image and removing its low frequency sub-band the microcalcifications are enhanced. Analyzing the effects of perturbations on high frequency subband it's possible to classify its borders as smooth, rugged or undefined. Results show a false positive reduction of 69.27% using a region growing algorithm. © 2008 IEEE.

Predictive value for malignancy in the finding of microcalcifications on ultrasonography of thyroid nodules

Background. The am of this study was to determine the predictive value for malignancy of microcalcifications determined by ultrasonography in thyroid nodules. Methods. One hundred seventy-seven nodules were prospectively studied by ultrasonography and compared with their fine-needle aspirative biopsy. The association between the presence and type of calcification and cytologic findings was verified through the chi-square test or likelihood ratio. Results. Thirty nodules showed calcification, of which 17 had fine calcifications, 3 had fine and gross calcifications, and 10 had only coarse calcification. Seven (41.18%) of 17 fine calcified nodules were malignant on cytology, 8 (47.06%) were benign, 1 (5,88%) was indeterminate, and 1 was suspect for malignancy. We found statistical significance between the presence of fine calcifications and malignancy (p =.001) and, in the 13 malignant nodule group, 8 (61.50%) had fine calcifications. Conclusion. This study suggests that microcalcifications were highly specific for malignancy and were present in 61% of the malignant nodules. (c) 2008 Wiley Periodicals, Inc.

Using a NPWE model observer to assess suitable image quality for a digital mammography quality assurance programme.

A method of objectively determining imaging performance for a mammography quality assurance programme for digital systems was developed. The method is based on the assessment of the visibility of a spherical microcalcification of 0.2 mm using a quasi-ideal observer model. It requires the assessment of the spatial resolution (modulation transfer function) and the noise power spectra of the systems. The contrast is measured using a 0.2-mm thick Al sheet and Polymethylmethacrylate (PMMA) blocks. The minimal image quality was defined as that giving a target contrast-to-noise ratio (CNR) of 5.4. Several evaluations of this objective method for evaluating image quality in mammography quality assurance programmes have been considered on computed radiography (CR) and digital radiography (DR) mammography systems. The measurement gives a threshold CNR necessary to reach the minimum standard image quality required with regards to the visibility of a 0.2-mm microcalcification. This method may replace the CDMAM image evaluation and simplify the threshold contrast visibility test used in mammography quality.

Modèles murins de prééclampsie et effets préventifs de l’entraînement physique

La prééclampsie est la première cause de mortalité et de morbidité périnatale et aucun traitement, mis à part l’accouchement, n’est connu à ce jour. Pour mieux comprendre cette maladie, nous avons utilisé trois modèles animaux. Dans un premier temps, nous avons voulu confirmer la présence de prééclampsie chez les souris déficientes en p57kip2, une protéine impliquée dans le cycle cellulaire des trophoblastes. Contrairement au groupe japonais, l’hypertension et la protéinurie au cours de la gestation ne survenaient pas, malgré une perte de structure des trophoblastes dans le labyrinthe ainsi qu’une microcalcification au niveau de leurs placentas. Nous avons alors observé que la diète japonaise induisait à elle seule une diminution de la croissance fœtale, ainsi qu’une dysfonction endothéliale chez ces souris. Nos résultats démontrent que ni les altérations placentaires, ni la génétique ne sont suffisantes pour induire les symptômes de la prééclampsie dans ce modèle, et que la diète peut avoir des effets délétères chez la souris gestante peu importe le génotype. Ensuite, nous avons démontré que les souris hypertendues surexprimant la rénine et l’angiotensinogène humaine développent de la protéinurie et une augmentation de la pression artérielle au cours de la gestation. Leurs placentas sont affectés par de la nécrose et une perte de structure des trophoblastes du labyrinthe en plus de surexprimer le gène du récepteur sFlt-1. Ces souris représentent le premier modèle animal de prééclampsie superposée à de l’hypertension chronique. Finalement, en utilisant des femelles normotendues surexprimant l’angiotensinogène humaine qui développent les symptômes de la prééclampsie lorsqu’elles sont accouplées à des mâles qui surexpriment la rénine humaine, nous avons établi que l’entraînement physique normalisait la hausse de pression ainsi que l’apparition de protéinurie en fin de gestation. Aussi, l'entraînement améliorait la croissance fœtale et placentaire ainsi que la réponse vasculaire indépendante de l’endothélium, et ce, indépendamment du génotype des souris. La présence d’une prolifération exagérée et désorganisée des trophoblastes dans ce modèle était aussi normalisée. L’entraînement physique prévient donc l’apparition des symptômes de la prééclampsie dans ce modèle. Mis ensemble, nos résultats aideront à mieux comprendre les mécanismes à l’origine de la prééclampsie et de sa prévention.

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.

Caracterizacion de nódulos tiroideos: concordancia entre estudio citopatologico y ecografia en la detección de nódulos malignos

: Los métodos imagenológicos para evaluar los nódulos tiroideos han sido motivo de estudio en las últimas décadas, especialmente la ecografía sobresale sobre las otras modalidades diagnósticas por su accesibilidad, portabilidad, y seguridad. A pesar de ello, las características ecográficas de cada nódulo han sido objeto de controversia en cuanto a su potencial detección de malignidad o benignidad. Se presenta un estudio de concordancia entre el estudio citopatológico y la ecografía para la caracterización nódulos tiroideos de naturaleza maligna y benigna, y su análisis de pruebas diagnósticas. Metodología: Se realizó un estudio descriptivo de concordancia con estudio de pruebas diagnósticas anidado. Se escogieron todos los pacientes con nódulos tiroideos a quienes se les realizó ecografía y estudio citopatológico de la lesión y se estudiaron los hallazgos ecográficos para evaluar su potencial diagnóstico para malignidad. Se incluyeron un total de 100 pacientes con nódulos tiroideos potencialmente malignos. La concordancia entre la ecografía en modo B y el estudio citopatológico fue moderada (índice kappa 0.55). La característica con mayor potencial para detectar malignidad fue la presencia de Microcalcificaciones (sensibilidad 75%, especificidad 92%).

Estudo descritivo das mamografias categorias IV e V da classificação BI-RADS

Pós-graduação em Ginecologia, Obstetrícia e Mastologia - FMB

Um método para melhoria de qualidade de imagens médicas utilizando a transformada wavelet

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Distúrbios nutricionais e câncer de mama: deficiência de zinco

Pós-graduação em Ginecologia, Obstetrícia e Mastologia - FMB

The performance of computer-aided detection when analyzing prior mammograms of newly detected breast cancers with special focus on the time interval from initial imaging to detection

PURPOSE: The clinical role of CAD systems to detect breast cancer, which have not been on cancer containing mammograms not detected by the radiologist was proven retrospectively. METHODS: All patients from 1992 to 2005 with a histologically verified malignant breast lesion and a mammogram at our department, were analyzed in retrospect focussing on the time of detection of the malignant lesion. All prior mammograms were analyzed by CAD (CADx, USA). The resulting CAD printout was matched with the cancer containing images yielding to the radiological diagnosis of breast cancer. CAD performance, sensitivity as well as the association of CAD and radiological features were analyzed. RESULTS: 278 mammograms fulfilled the inclusion criteria. 111 cases showed a retrospectively visible lesion (71 masses, 23 single microcalcification clusters, 16 masses with microcalcifications, in one case two microcalcification clusters). 54/87 masses and 34/41 microcalcifications were detected by CAD. Detection rates varied from 9/20 (ACR 1) to 5/7 (ACR 4) (45% vs. 71%). The detection of microcalcifications was not influenced by breast tissue density. CONCLUSION: CAD might be useful in an earlier detection of subtle breast cancer cases, which might remain otherwise undetected.