Breast cancer occurs with high frequency among the world's population and its effects impact the patients' perception of their own sexuality and their very personal image. This work presents a computational methodology that helps specialists detect breast masses in mammogram images. The first stage of the methodology aims to improve the mammogram image. This stage consists in removing objects outside the breast, reducing noise and highlighting the internal structures of the breast. Next, cellular neural networks are used to segment the regions that might contain masses. These regions have their shapes analyzed through shape descriptors (eccentricity, circularity, density, circular disproportion and circular density) and their textures analyzed through geostatistic functions (Ripley's K function and Moran's and Geary's indexes). Support vector machines are used to classify the candidate regions as masses or non-masses, with sensitivity of 80%, rates of 0.84 false positives per image and 0.2 false negatives per image, and an area under the ROC curve of 0.87.
KESUCZ, F.-N. Theoretical and experimental aspects of the controlled movement of solenoid actuators by means of the voltage static converter with logic gates.
Breast cancer shows high frequency and its psychological effects affect the female's perception of sexuality and their personal image. The mammographic images processing has contributed to the detection and diagnosis of breast nodules, and contributes as an important tool, reducing the diagnosis uncertainty. This work presents a computational methodology that helps the expert in the task of mass detection based on mammographic images. To achieve this, Hidden Markov Model and Ripley's K function were used to detect masses, segmented by Cellular Neural Networks. In the tests methodology demonstrate a sensitivity of 94.62%, with 92.57% of specificity, 93.60% of accuracy rate and an average of 0.53 false positives per image.
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