Boussetta Sana

Bio: Boussetta Sana is an academic researcher from Tunis University. The author has contributed to research in topics: Cancer & Mammography. The author has an hindex of 1, co-authored 1 publications receiving 36 citations.

Breast cancer detection: A review on mammograms analysis techniques

Abstract: Breast cancer is the most common cancer among women over 40 years. Studies have shown that early detection and appropriate treatment of breast cancer significantly increase the chances of survival. They have also shown that early detection of small lesions boosts prognosis and leads to a significant reduction in mortality. Mammography is in this case the best diagnostic technique for screening. However, the interpretation of mammograms is not easy because of small differences in densities of different tissues within the image. This is especially true for dense breasts. This paper is a survey of the automatic early detection of breast cancer by analyzing mammographic images. This analysis could provide radiologists a better understanding of stereotypes and provides, if it is detected at an early stage, a better prognosis inducing a significant decrease in mortality.

A topographic representation for mammogram segmentation

Abstract: This paper presents a novel segmentation method for delineating regions of interest (ROI’s) in mammograms. The algorithm concurrently detects the breast boundary, the pectoral muscle and dense regions that include candidate masses. The resulting segmentation constitutes an analysis of the global structure of the object in the mammogram. We propose a topographic representation called the iso-level contour map, in which a salient region forms a dense quasi-concentric pattern of contours. The topological and geometrical structure of the image is analysed using an inclusion tree that is a hierarchical representation of the enclosure relationships between contours. The “saliency” of the region is measured topologically as the minimum nesting depth. Experimental results demonstrate that the proposed method achieves a satisfactory performance as a prompt system in the mass detection.

A New CAD System for Breast Microcalcifications Diagnosis

Abstract: Breast cancer is one of the most deadly cancers in the world, especially among women. With no identified causes and absence of effective treatment, early detection remains necessary to limit the damages and provide possible cure. Submitting women with family antecedent to mammography periodically can provide an early diagnosis of breast tumors. Computer Aided Diagnosis (CAD) is a powerful tool that can help radiologists improving their diagnostic accuracy at earlier stages. Several works have been developed in order to analyze digital mammographies, detect possible lesions (especially masses and microcalcifications) and evaluate their malignancy. In this paper a new approach of breast microcalcifications diagnosis on digital mammograms is introduced. The proposed approach begins with a preprocessing procedure aiming artifacts and pectoral muscle removal based on morphologic operators and contrast enhancement based on galactophorous tree interpolation. The second step of the proposed CAD system consists on segmenting microcalcifications clusters, using Generalized Gaussian Density (GGD) estimation and a Bayesian back-propagation neural network. The last step is microcalcifications characterization using morphologic features which are used to feed a neuro-fuzzy system to classify the detected breast microcalcifications into benign and malignant classes.

Breast density classification using Laws' mask texture features

Abstract: Characterisation of tissue density is clinically significant as high density is associated with the risk of developing breast cancer and also masks lesions. Accordingly, in the present work, PCA-kNN and PCA-NN based computer aided diagnostic (CAD) systems for breast tissue density classification have been proposed. The work has been carried out on the MIAS dataset. Five statistical texture features mean, standard deviation, entropy, kurtosis and skewness are evaluated from Laws' texture energy images resulting from Laws' masks of lengths 3, 5, 7 and 9. Principal component analysis is then applied to these texture feature vectors for feature space dimensionality reduction. The kNN classifier and the NN classifier are used for the classification task. The highest classification accuracy of 95.6% is achieved by using the first 8 principal components computed from texture features derived from Law's mask of length 5 for k = 8 using the kNN classifier.