Breast asymmetry analysis employing tree-structured wavelet transform

Statistically distributed developmental asymmetries appearing in paired body structures such as breasts in women are usually related to unhealthy biological conditions. In particular, there is evidence that breast cancer patients show more breast dimensional asymmetries and larger breasts than age-matched healthy women. It was also recently reported the application of Gabor nonorthogonal wavelets filtering for analyzing asymmetries of the directional structures appearing in mammograms corresponding to healthy and breast cancer patients. In this paper the Tree-Structured Wavelet Transform, which uses orthogonal wavelet bases, is applied for the first time in order to quantify texture asymmetries between left- and right- mammograms in women. In order to assess the suitability of the method, it was applied to characterize breast asymmetries of mammograms corresponding to a small population of 63 healthy women with ages ranging between 30 and 70 years. The obtained results justify further improvements of this method and its application for correlating asymmetries and the predisposition to breast cancer.Eje: Aplicaciones biomédicasRed de Universidades con Carreras en Informática (RedUNCI

Hybrid Discrete Wavelet Transform and Gabor Filter Banks Processing for Features Extraction from Biomedical Images

A new methodology for automatic feature extraction from biomedical images and subsequent classification is presented. The approach exploits the spatial orientation of high-frequency textural features of the processed image as determined by a two-step process. First, the two-dimensional discrete wavelet transform(DWT) is applied to obtain the HH high-frequency subband image. Then, a Gabor filter bank is applied to the latter at different frequencies and spatial orientations to obtain new Gabor-filtered image whose entropy and uniformity are computed. Finally, the obtained statistics are fed to a support vector machine (SVM) binary classifier. The approach was validated on mammograms, retina, and brain magnetic resonance (MR) images.The obtained classification accuracies show better performance in comparison to common approaches that use only the DWT or Gabor filter banks for feature extraction

Breast asymmetry analysis employing tree-structured wavelet transform

Statistically distributed developmental asymmetries appearing in paired body structures such as breasts in women are usually related to unhealthy biological conditions. In particular, there is evidence that breast cancer patients show more breast dimensional asymmetries and larger breasts than age-matched healthy women. It was also recently reported the application of Gabor nonorthogonal wavelets filtering for analyzing asymmetries of the directional structures appearing in mammograms corresponding to healthy and breast cancer patients. In this paper the Tree-Structured Wavelet Transform, which uses orthogonal wavelet bases, is applied for the first time in order to quantify texture asymmetries between left- and right- mammograms in women. In order to assess the suitability of the method, it was applied to characterize breast asymmetries of mammograms corresponding to a small population of 63 healthy women with ages ranging between 30 and 70 years. The obtained results justify further improvements of this method and its application for correlating asymmetries and the predisposition to breast cancer.Eje: Aplicaciones biomédicasRed de Universidades con Carreras en Informática (RedUNCI

Incorporating Breast Asymmetry Studies into CADx Systems

Breast cancer is one of the global leading causes of death among women, and an early detection is of uttermost importance to reduce mortality rates. Screening mammograms, in which radiologists rely only on their eyesight, are one of the most used early detection methods. However, characteristics, such as the asymmetry between breasts, a feature that could be very difficult to visually quantize, is key to breast cancer detection. Due to the highly heterogeneous and deformable structure of the breast itself, incorporating asymmetry measurements into an automated detection system is still a challenge. In this study, we proposed the use of a bilateral registration algorithm as an effective way to automatically measure mirror asymmetry. Furthermore, this information was fed to a machine learning algorithm to improve the accuracy of the model. In this study, 449 subjects (197 with calcifications, 207 with masses, and 45 healthy subjects) from a public database were used to train and evaluate the proposed methodology. Using this procedure, we were able to independently identify subjects with calcifications (accuracy = 0.825, AUC = 0.882) and masses (accuracy = 0.698, AUC = 0.807) from healthy subjects

Hybrid Image Mining Methods to Classify the Abnormality in Complete Field Image Mammograms Based on Normal Regions

Breast Cancer now becomes a common disease among woman in developing as well as developed countries. Many noninvasive methodologies have been used to detect breast cancer. Computer Aided diagnosis through, Mammography is a widely used as a screening tool and is the gold standard for the early detection of breast cancer. The classification of breast masses into the benign and malignant categories is an important problem in the area of computer-aided diagnosis of breast cancer. We present a new method for complete total image of mammogram analysis. A mammogram is analyzed region by region and is classified as normal or abnormal. We present a hybrid technique for extracting features that can be used to distinguish normal and abnormal regions of a mammogram. We describe our classifier technique that uses a unique re-classification method to boost the classification performance. Our proposed hybrid technique comprises decision tree followed by association rule miner shows most proficient and promising performance with high classification rate compared to many other classifiers. We have tested this technique on a set of ground-truth complete total image of mammograms and the result was quite effective

Clasificación de lesiones de piel basada en filtros de Gabor y color

CONGRESO ANUAL DE LA SOCIEDAD ESPAÑOLA DE INGENIERÍA BIOMÉDICA (CASEIB 2009) (27) (27.2009.CADIZ, ESPAÑA)Cuando se pretende diagnosticar un posible cáncer de piel, los médicos evalúan la lesión siguiendo diferentes reglas. Aunque la más famosa es la regla ABCD (Asimetría, Borde, Color, Diámetro), una técnica muy empleada en Dermatología es clasificar las lesiones siguiendo un análisis de patrones. Este artículo presenta un método novedoso basado en técnicas de filtrado que clasifica imágenes de color correspondientes a diferentes patrones dermatoscópicos. Hemos evaluado nuestro método usando filtros de Gabor y hemos comparado los resultados obtenidos cuando usamos dos espacios diferentes de color (RGB y L*a*b*) y también cuando consideramos o no la información de color. Para implementar esta tarea hemos evaluado la tasa de clasificación usando 8 vectores diferentes de características. Para cada tipo de vector de características hemos usado el 80% de las imágenes de la base de datos para entrenar una red neuronal fuzzy ARTMAP. El restante 20% de las imágenes fue usado para testear la red. La mejor tasa de clasificación es del 90% cuando usamos el espacio de color L*a*b* para la representación de las imágenes.Junta de Andalucía P06-TIC-0141

Contralateral asymmetry for breast cancer detection : A CADx approach

Early detection is fundamental for the effective treatment of breast cancer and the screening mammography is the most common tool used by the medical community to detect early breast cancer development. Screening mammograms include images of both breasts using two standard views, and the contralateral asymmetry per view is a key feature in detecting breast cancer. we propose a methodology to incorporate said asymmetry information into a computer-aided diagnosis system that can accurately discern between healthy subjects and subjects at risk of having breast cancer. Furthermore, we generate features that measure not only a view-wise asymmetry, but a subject-wise one. Briefly, the methodology co-registers the left and right mammograms, extracts image characteristics, fuses them into subjectwise features, and classifies subjects. In this study, 152 subjects from two independent databases, one with analog- and one with digital mammograms, were used to validate the methodology. Areas under the receiver operating characteristic curve of 0.738 and 0.767, and diagnostic odds ratios of 23.10 and 9.00 were achieved, respectively. In addition, the proposed method has the potential to rank subjects by their probability of having breas

COMPUTER AIDED SYSTEM FOR BREAST CANCER DIAGNOSIS USING CURVELET TRANSFORM

Breast cancer is a leading cause of death among women worldwide. Early detection is the key for improving breast cancer prognosis. Digital mammography remains one of the most suitable tools for early detection of breast cancer. Hence, there are strong needs for the development of computer aided diagnosis (CAD) systems which have the capability to help radiologists in decision making. The main goal is to increase the diagnostic accuracy rate. In this thesis we developed a computer aided system for the diagnosis and detection of breast cancer using curvelet transform. Curvelet is a multiscale transform which possess directionality and anisotropy, and it breaks some inherent limitations of wavelet in representing edges in images. We started this study by developing a diagnosis system. Five feature extraction methods were developed with curvelet and wavelet coefficients to differentiate between different breast cancer classes. The results with curvelet and wavelet were compared. The experimental results show a high performance of the proposed methods and classification accuracy rate achieved 97.30%. The thesis then provides an automatic system for breast cancer detection. An automatic thresholding algorithm was used to separate the area composed of the breast and the pectoral muscle from the background of the image. Subsequently, a region growing algorithm was used to locate the pectoral muscle and suppress it from the breast. Then, the work concentrates on the segmentation of region of interest (ROI). Two methods are suggested to accomplish the segmentation stage: an adaptive thresholding method and a pattern matching method. Once the ROI has been identified, an automatic cropping is performed to extract it from the original mammogram. Subsequently, the suggested feature extraction methods were applied to the segmented ROIs. Finally, the K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) classifiers were used to determine whether the region is abnormal or normal. At this level, the study focuses on two abnormality types (mammographic masses and architectural distortion). Experimental results show that the introduced methods have very high detection accuracies. The effectiveness of the proposed methods has been tested with Mammographic Image Analysis Society (MIAS) dataset. Throughout the thesis all proposed methods and algorithms have been applied with both curvelet and wavelet for comparison and statistical tests were also performed. The overall results show that curvelet transform performs better than wavelet and the difference is statistically significant