Breast elastography is a new sonographic technique that provides additional information to evaluate tissue stiffness. However, interpreting breast elastography images can vary depending on the radiologist. In order to provide quantitative and less subjective data regarding the stiffness of a lesion, we developed a tool to measure the amount of hard area in a lesion from the 2D image. The database consisted of 78 patients with 83 breast lesions (31 malignant and 52 benign). Two radiologists and one resident manually drew the contour of the lesions in B-mode ultrasound images and the contour was mapped in the elastography image. By using the system proposed, the radiologists obtained a very good diagnostic agreement among themselves (kappa = 0.86), achieving the same sensitivity and specificity (80.7 and 88.5, respectively), and an AUC of 0.883 for Radiologist 1 and 0.892 for Radiologist 2. The Resident had less interobserver agreement, as well as lower specificity and AUC, which may be related to less experience. Furthermore, the radiologists had an agreement with the tool used in the automatic method higher than 90%. Thus, the method developed was useful in aiding the diagnosis of breast lesions in strain elastography, minimizing its subjectivity.
Numerous breast phantoms have been developed to be as realistic as possible to ensure the accuracy of image quality analysis, covering a greater range of applications. In this study, we simulated three different densities of the breast parenchyma using paraffin gel, acrylic plates and PVC films. Hydroxyapatite was used to simulate calcification clusters. From the images acquired with a GE Senographe DR 2000D mammography system, we selected 68 regions of interest (ROIs) with and 68 without a simulated calcification cluster. To validate the phantom simulation, we selected 136 ROIs from the University of South Florida’s Digital Database for Screening Mammography (DDSM). Seven trained observers performed two observer experiments by using a high-resolution monitor Barco mod. E-3620. In the first experiment, the observers had to distinguish between real or phantom ROIs (with and without calcification). In the second one, the observers had to indicate the ROI with calcifications between a pair of ROIs. Results from our study show that the hydroxyapatite calcifications had poor contrast in the simulated breast parenchyma, thus observers had more difficulty in identifying the presence of calcification clusters in phantom images. Preliminary analysis of the power spectrum was conducted to investigate the radiographic density and the contrast thresholds for calcification detection. The values obtained for the power spectrum exponent (β) were comparable with those found in the literature.
In this work some segmentation techniques are evaluated by using a simple centroid-based classification system regarding breast mass delineation in digital mammography images. The aim is to determine the best one for future CADx developments. Six techniques were tested: Otsu, SOM, EICAMM, Fuzzy C-Means, K-Means and Level-Set. All of them were applied to segment 317 mammography images from DDSM database. A single compact set of attributes was extracted and two centroids were defined, one for malignant and another for benign cases. The final classification was based on proximity with a given centroid and the best results were presented by the Level-Set technique with a 68.1% of Accuracy, which indicates this method as the most promising for breast masses segmentation aiming a more precise interpretation in schemes CADx.
The task of identifying the malignancy of nodular lesions on mammograms becomes quite complex due to overlapped structures or even to the granular fibrous tissue which can cause confusion in classifying masses shape, leading to unnecessary biopsies. Efforts to develop methods for automatic masses detection in CADe (Computer Aided Detection) schemes have been made with the aim of assisting radiologists and working as a second opinion. The validation of these methods may be accomplished for instance by using databases with clinical images or acquired through breast phantoms. With this aim, some types of materials were tested in order to produce radiographic phantom images which could characterize a good enough approach to the typical mammograms corresponding to actual breast nodules. Therefore different nodules patterns were physically produced and used on a previous developed breast phantom. Their characteristics were tested according to the digital images obtained from phantom exposures at a LORAD M-IV mammography unit. Two analysis were realized the first one by the segmentation of regions of interest containing the simulated nodules by an automated segmentation technique as well as by an experienced radiologist who has delineated the contour of each nodule by means of a graphic display digitizer. Both results were compared by using evaluation metrics. The second one used measure of quality Structural Similarity (SSIM) to generate quantitative data related to the texture produced by each material. Although all the tested materials proved to be suitable for the study, the PVC film yielded the best results.
Due to the high incidence rate of breast cancer in women, many procedures have been developed to assist the diagnosis and early detection. Currently, ultrasonography has proved as a useful tool in distinguishing benign and malignant masses. In this context, the computer-aided diagnosis schemes have provided to the specialist a second opinion more accurately and reliably, minimizing the visual subjectivity between observers. Thus, we propose the application of an automatic detection method based on the use of the technique of active contour in order to show precisely the contour of the lesion and provide a better understanding of their morphology. For this, a total of 144 images of phantoms were segmented and submitted to morphological operations of opening and closing for smoothing the edges. Then morphological features were extracted and selected to work as input parameters for the neural classifier Multilayer Perceptron which obtained 95.34% correct classification of data and Az of 0.96.
Ultrasound (US) is a useful diagnostic tool to distinguish benign from malignant breast masses, providing more detailed evaluation in dense breasts. Due to the subjectivity in the images interpretation, computer-aid diagnosis (CAD) schemes have been developed, increasing the mammography analysis process to include ultrasound images as complementary exams. As one of most important task in the evaluation of this kind of images is the mass detection and its contours interpretation, automated segmentation techniques have been investigated in order to determine a quite suitable procedure to perform such an analysis. Thus, the main goal in this work is investigating the effect of some processing techniques used to provide information on the determination of suspicious breast lesions as well as their accurate boundaries in ultrasound images. In tests, 80 phantom and 50 clinical ultrasound images were preprocessed, and 5 segmentation techniques were tested. By using quantitative evaluation metrics the results were compared to a reference image delineated by an experienced radiologist. A self-organizing map artificial neural network has provided the most relevant results, demonstrating high accuracy and low error rate in the lesions representation, corresponding hence to the segmentation process for US images in our CAD scheme under tests.