A New Approach to the Detection of Mammogram Boundary

Mammography is a method used for the detection of breast cancer. computer-aided diagnostic (CAD) systems help the radiologist in the detection and interpretation of mass in breast mammography. One of the important information of a mass is its contour and its form because it provides valuable information about the abnormality of a mass. The accuracy in the recognition of the shape of a mass is related to the accuracy of the detected mass contours. In this work we propose a new approach for detecting the boundaries of lesion in mammography images based on region growing algorithm without using the threshold, the proposed method requires an initial rectangle surrounding the lesion selected manually by the radiologist (Region Of Interest), where the region growing algorithm applies on lines segments that attach each pixel of this rectangle with the seed point, such as the ends (seeds) of each line segment grow in a direction towards one another. The proposed approach is evaluated on a set of data with 20 masses of the MIAS base whose contours are annotated manually by expert radiologists. The performance of the method is evaluated in terms of specificity, sensitivity, accuracy and overlap. All the findings and details of approach are presented in detail

Speed up Robust Features (SURF) with Principal Component Analysis-Support Vector Machine (PCA-SVM) for benign and malignant classifications

A novel Computer Aided Diagnosis (CADx) component is proposed for breast cancer classifications. Four major phases were conducted in this research. The first phase is pre-processing, this is followed by features extraction phase by using the Speed Up Robust Features (SURF). The next phase is features selection by using the Principal Component Analysis (PCA). The final phase is the classification phase to classify the cancer. Three different classifiers; Support Vector Machine (SVM), Linear Discriminant Analysis (LDA) and Decision Tree (DT) were compared in this research. Results obtained shows that the PCA-SVM performs the highest accuracy with 92.9% accurate compared to other classifiers.Keywords: breast cancer; CADx; SURF; PCA; SV

Técnicas basadas en kernel para el análisis de texturas en imagen biomédica

[Resumen] En problemas del mundo real es relevante el estudio de la importancia de todas las variables obtenidas de manera que sea posible la eliminación de ruido, es en este punto donde surgen las técnicas de selección de variables. El objetivo de estas técnicas es pues encontrar el subconjunto de variables que describan de la mejor manera posible la información útil contenida en los datos permitiendo mejorar el rendimiento. En espacios de alta dimensionalidad son especialmente interesantes las técnicas basadas en kernel, donde han demostrado una alta eficiencia debido a su capacidad para generalizar en dichos espacios. En este trabajo se realiza una nueva propuesta para el análisis de texturas en imagen biomédica mediante la integración, utilizando técnicas basadas en kernel, de diferentes tipos de datos de textura para la selección de las variables más representativas con el objetivo de mejorar los resultados obtenidos en clasificación y en interpretabilidad de las variables obtenidas. Para validar esta propuesta se ha formalizado un diseño experimental con cuatro fases diferenciadas: extracción y preprocesado de los datos, aprendizaje y selección del mejor modelo asegurando la reproducibilidad de los resultados a la vez que una comparación en condiciones de igualdad.[Resumo] En problemas do mundo real é relevante o estudo da importancia de todas as variables obtidas de maneira que sexa posible a eliminación de ruído, é neste punto onde xorden as técnicas de selección de variables. O obxectivo destas técnicas é pois encontrar o subconxunto de variables que describan do mellor xeito posible a información útil contida nos datos permitindo mellorar o rendemento. En espazos de alta dimensionalidade son especialmente interesantes as técnicas baseadas en kernel, onde demostraron unha alta eficiencia debido á súa capacidade para xeneralizar nos devanditos espazos. Neste traballo realízase unha nova proposta para a análise de texturas en imaxe biomédica mediante a integración, utilizando técnicas baseadas en kernel, de diferentes tipos de datos de textura para a selección das variables máis representativas co obxectivo de mellorar os resultados obtidos en clasificación e en interpretabilidade das variables obtidas. Para validar esta proposta formalizouse un deseño experimental con catro fases diferenciadas: extracción e preprocesar dos datos, aprendizaxe e selección do mellor modelo asegurando a reproducibilidade dos resultados á vez que unha comparación en condicións de igualdade. Utilizáronse imaxes de xeles de electroforese bidimensional.[Abstract] In real-world problems it is of relevance to study the importance of all the variables obtained, so that denoising could be possible, because it is at this point when the variable selection techniques arise. Therefore, these techniques are aimed at finding the subset of variables that describe' in the best possible way the useful information contained in the data, allowing improved performance. In high-dimensional spaces, the kernel-based techniques are of special relevance, as they have demonstrated a high efficiency due to their ability to generalize in these spaces. In this work, a new approach for texture analysis in biomedical imaging is performed by means of integration. For this procedure, kernel-based techniques were used with different types of texture data for the selection of the most representative variables in order to improve the results obtained in classification and interpretability of the obtained variables. To validate this proposal, an experimental design has been concluded, consisting of four different phases: 1) Data extraction; 2) Data pre-processing; 3) Learning and 4) Selection of the best model to ensure the reproducibility of results while making a comparison under conditions of equality. In this regard, two-dimensional electrophoresis gel images have been used