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.

Development of model observers applied to 3D breast tomosynthesis microcalcifications and masses

The development of model observers for mimicking human detection strategies has followed from symmetric signals in simple noise to increasingly complex backgrounds. In this study we implement different model observers for the complex task of detecting a signal in a 3D image stack. The backgrounds come from real breast tomosynthesis acquisitions and the signals were simulated and reconstructed within the volume. Two different tasks relevant to the early detection of breast cancer were considered: detecting an 8 mm mass and detecting a cluster of microcalcifications. The model observers were calculated using a channelized Hotelling observer (CHO) with dense difference-of-Gaussian channels, and a modified (Partial prewhitening [PPW]) observer which was adapted to realistic signals which are not circularly symmetric. The sustained temporal sensitivity function was used to filter the images before applying the spatial templates. For a frame rate of five frames per second, the only CHO that we calculated performed worse than the humans in a 4-AFC experiment. The other observers were variations of PPW and outperformed human observers in every single case. This initial frame rate was a rather low speed and the temporal filtering did not affect the results compared to a data set with no human temporal effects taken into account. We subsequently investigated two higher speeds at 5, 15 and 30 frames per second. We observed that for large masses, the two types of model observers investigated outperformed the human observers and would be suitable with the appropriate addition of internal noise. However, for microcalcifications both only the PPW observer consistently outperformed the humans. The study demonstrated the possibility of using a model observer which takes into account the temporal effects of scrolling through an image stack while being able to effectively detect a range of mass sizes and distributions.

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

Correlação radiológica e histológica de lesões mamárias não-palpáveis em pacientes submetidas a marcação pré-cirúrgica, utilizando-se o sistema BI-RADS

OBJECTIVE: To evaluate the positive predictive value for BI-RADS (Breast Imaging Reporting and Data System) categories 3, 4 and 5, correlating mammographic and histological diagnosis in non-palpable breast lesions. MATERIALS AND METHODS: Analytical-descriptive study of 169 women submitted to stereotactic localization for surgical biopsy of non-palpable breast lesions. Mammographic and histological findings were correlated, analyzing the predictive positive value for each category. RESULTS: Forty-two (24.8%) cases were diagnosed with breast cancer - only one in category 3, 19 in category 4, and 22 in category 5. The positive predictive value for categories 3, 4A, 4B, 4C and 5 were, respectively, 3.4%, 10.3%, 11.3%, 36% and 91.7%. Microcalcifications were the most frequent finding related to malignancy, present in 61.5% of these cases. CONCLUSION: The present study has demonstrated that BI-RADS allows a safe prediction of high suspicion of malignancy in lesions category 5 and low suspicion for category 3. As regards the category 4, the positive predictive value has shown a progressive increase in subcategories A, B and C, demonstrating that this subclassification represents an invaluable contribution for a more detailed and accurate assessment of lesions suspicious for malignancy.

Computational adjustment technique for digital mammographic images based on the digitizer characteristic curve

We evaluated the performance of a novel procedure for segmenting mammograms and detecting clustered microcalcifications in two types of image sets obtained from digitization of mammograms using either a laser scanner, or a conventional ""optical"" scanner. Specific regions forming the digital mammograms were identified and selected, in which clustered microcalcifications appeared or not. A remarkable increase in image intensity was noticed in the images from the optical scanner compared with the original mammograms. A procedure based on a polynomial correction was developed to compensate the changes in the characteristic curves from the scanners, relative to the curves from the films. The processing scheme was applied to both sets, before and after the polynomial correction. The results indicated clearly the influence of the mammogram digitization on the performance of processing schemes intended to detect microcalcifications. The image processing techniques applied to mammograms digitized by both scanners, without the polynomial intensity correction, resulted in a better sensibility in detecting microcalcifications in the images from the laser scanner. However, when the polynomial correction was applied to the images from the optical scanner, no differences in performance were observed for both types of images. (C) 2008 SPIE and IS&T [DOI: 10.1117/1.3013544]

Classification of breast masses using a committee machine of artificial neural networks

This work proposes a new approach using a committee machine of artificial neural networks to classify masses found in mammograms as benign or malignant. Three shape factors, three edge-sharpness measures, and 14 texture measures are used for the classification of 20 regions of interest (ROIs) related to malignant tumors and 37 ROIs related to benign masses. A group of multilayer perceptrons (MLPs) is employed as a committee machine of neural network classifiers. The classification results are reached by combining the responses of the individual classifiers. Experiments involving changes in the learning algorithm of the committee machine are conducted. The classification accuracy is evaluated using the area A. under the receiver operating characteristics (ROC) curve. The A, result for the committee machine is compared with the A, results obtained using MLPs and single-layer perceptrons (SLPs), as well as a linear discriminant analysis (LDA) classifier Tests are carried out using the student's t-distribution. The committee machine classifier outperforms the MLP SLP, and LDA classifiers in the following cases: with the shape measure of spiculation index, the A, values of the four methods are, in order 0.93, 0.84, 0.75, and 0.76; and with the edge-sharpness measure of acutance, the values are 0.79, 0.70, 0.69, and 0.74. Although the features with which improvement is obtained with the committee machines are not the same as those that provided the maximal value of A(z) (A(z) = 0.99 with some shape features, with or without the committee machine), they correspond to features that are not critically dependent on the accuracy of the boundaries of the masses, which is an important result. (c) 2008 SPIE and IS&T.

Breast phantom with silicone implant for evaluation in conventional mammography

With the increased incidence of cancer and a similarly increased number of surgeries for insertion of silicone breast implants, it is necessary to assess the effect of such material within the breast tissue, particularly in mammography, because of the reduction in the power of breast cancer diagnosis. In this work, we introduce a breast phantom with silicone implants in order to evaluate the influence of the implant on the visibility of the main mammographic findings: fibers, microcalcifications and tumor masses. In this proposed phantom, the breast tissue was simulated using gel paraffin. In the optical density of phantom mammograms with implants, a reduction in breast tissue visibility was seen corresponding to 23% when compared to a phantom without silicone implants. This poor visibility was due to the X-ray beam scattering on silicone material; this effect produced a loss of visibility in the areas adjacent to the implant. It is expected that the proposed phantom model may be used as a device for the establishment of a technical standard for these types of procedures.

Using data mining techniques to support breast cancer diagnosis

More than ever, there is an increase of the number of decision support methods and computer aided diagnostic systems applied to various areas of medicine. In breast cancer research, many works have been done in order to reduce false-positives when used as a double reading method. In this study, we aimed to present a set of data mining techniques that were applied to approach a decision support system in the area of breast cancer diagnosis. This method is geared to assist clinical practice in identifying mammographic findings such as microcalcifications, masses and even normal tissues, in order to avoid misdiagnosis. In this work a reliable database was used, with 410 images from about 115 patients, containing previous reviews performed by radiologists as microcalcifications, masses and also normal tissue findings. Throughout this work, two feature extraction techniques were used: the gray level co-occurrence matrix and the gray level run length matrix. For classification purposes, we considered various scenarios according to different distinct patterns of injuries and several classifiers in order to distinguish the best performance in each case described. The many classifiers used were Naïve Bayes, Support Vector Machines, k-nearest Neighbors and Decision Trees (J48 and Random Forests). The results in distinguishing mammographic findings revealed great percentages of PPV and very good accuracy values. Furthermore, it also presented other related results of classification of breast density and BI-RADS® scale. The best predictive method found for all tested groups was the Random Forest classifier, and the best performance has been achieved through the distinction of microcalcifications. The conclusions based on the several tested scenarios represent a new perspective in breast cancer diagnosis using data mining techniques.