This paper describes part of content-based image retrieval (CBIR) system that has been developed for mammograms. Details are presented of methods implemented to derive measures of similarity based upon structural characteristics and distributions of density of the fibroglandular tissue, as well as the anatomical size and shape of the breast region as seen on the mammogram. Wellknown features related to shape, size, and texture (statistics of the gray-level histogram, Haralick's texture features, and moment-based features) were applied, as well as less-explored features based in the Radon domain and granulometric measures. The Kohonen self-organizing map (SOM) neural network was used to perform the retrieval operation. Performance evaluation was done using precision and recall curves obtained from comparison between the query and retrieved images. The proposed methodology was tested with 1,080 mammograms, including craniocaudal and mediolateral-oblique views. Precision rates obtained are in the range from 79% to 83% considering the total image set. Considering the first 50% of the retrieved mages, the precision rates are in the range from 78% to 83%; the rates are in the range from 79% to 86% considering the first 25% of the retrieved images. Results obtained indicate the potential of the implemented methodology to serve as a part of a CBIR system for mammography.KEY WORDS: Mammography, contend-based image retrieval, Kohonen self-organizing map, texture features, granulometric measures, radon transform domain, breast density INTRODUCTION: BREAST CANCER AND MAMMOGRAPHYB reast density has been shown to be a risk factor in the development of breast cancer. Wolfe 1 presented the first study relating the density and structure of breast tissues as seen on mammograms to the characteristics of breast disease: he described and illustrated cases associating patterns of parenchymal distortion with the risk of development of breast cancer. Since then, several other researchers have studied the relation between the structural composition of breast tissue and the abnormalities found in the related regions. 2 A consequence of the understanding of this relationship has been the development of systems for the description and analysis of the density patterns found in mammograms: the Breast Imaging Reporting and Data System (BI-RADS), 3 developed by the American College of Radiology, is the most important of such systems. BI-RADS contains recommendations for standardization of terms used in image-based diagnosis of breast diseases, the division of breast composition and mammographic findings into categories, and suggestions for further actions by the radiologist. Visual analysis of mammograms takes into consideration the shape and size of the breast, the conditions of the breast contour and the nipple position, and the distribution of fibroglandular tissue (degree of granularity, amount, and distribution of breast density). Notwithstanding the developments mentioned above, visual analysis of mammograms by radiologists remains sub...
In this paper, methods are presented for automatic detection of the nipple and the pectoral muscle edge in mammograms via image processing in the Radon domain. Radon-domain information was used for the detection of straight-line candidates with high gradient. The longest straight-line candidate was used to identify the pectoral muscle edge. The nipple was detected as the convergence point of breast tissue components, indicated by the largest response in the Radon domain. Percentages of false-positive (FP) and false-negative (FN) areas were determined by comparing the areas of the pectoral muscle regions delimited manually by a radiologist and by the proposed method applied to 540 mediolateral-oblique (MLO) mammographic images. The average FP and FN were 8.99% and 9.13%, respectively. In the detection of the nipple, an average error of 7.4 mm was obtained with reference to the nipple as identified by a radiologist on 1,080 mammographic images (540 MLO and 540 craniocaudal views).
This work presents the usefulness of texture features in the classification of breast lesions in 5518 images of regions of interest, which were obtained from the Digital Database for Screening Mammography that included microcalcifications, masses, and normal cases. Sixteen texture features were used, i.e., 13 were based on the spatial gray-level dependence matrix and 3 on the wavelet transform. The nonparametric K-NN classifier was used in the classification stage. The results obtained from receiver operating characteristic analysis indicated that the texture features can be used for separating normal regions and lesions with masses and microcalcifications, yielding the area under the curve (AUC) values of 0.957 and 0.859, respectively. However, the texture features were not very effective for distinguishing between malignant and benign lesions because the AUC was 0.617 for masses and 0.607 for microcalcifications. The study showed that the texture features can be used for the detection of suspicious regions in mammograms.
pol ice officers are exposed to impact noise coming from firearms, which may cause irreversible injuries to the hearing system. Aim:To evaluate the noise exposure in shooting stands during gunfire exercises, to analyze the acoustic impact of the noise produced by the firearms and to associate it with tonal audiometry results.Study design: Cross-sectional. Materials and methods:To measure noise intensity we used a digital sound level meter, and the acoustic analysis was carried out by means of the oscillations and cochlear response curves provided by the praat software. 30 police officers were selected (27 males and 3 females). Results:The peak level measured was 113.1 dB(C) from a .40 pistol and 116.8 dB(C) for a .38 revolver. The values obtained for oscillation and praat was 17.9±0.3 Barks, corresponding to the rate of 4,120 and 4,580 Hz. Audiometry indicated greater hearing loss at 4,000Hz in 86.7% of the cases. Conclusion:With the acoustic analysis it was possible to show cause and effect between the main areas of energy excitation of the cochlea (praat cochlear response curve) and the frequencies of low hearing acuity. Braz J Otorhinolaryngol. 2011;77(2):163-70. ORIgInAL ARTIcLE BJORL
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