Fuzzy C-means and region growing based classification of tumor from mammograms using hybrid texture feature

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 topographic representation for mammogram segmentation

Development of intelligent systems based on Bayesian regularization network and neuro-fuzzy models for mass detection in mammograms

Digital image watermarking can be considered as a better solution to protect authentication of images and its copyright protection. The purpose of proposed method is to provide better security of host image. Proposed method is implemented using DWT and DCT. Watermark image is inserted in LL sub-band of host image by dividing it using DWT. 8×8 block DCT is applied on LL sub-band and watermark is embedded into last pixel of each block. On the basis of watermark pixel value, pixel value of host image is affected. Here, binary watermark image is taken for experiment. Extraction process is completely non-blind technique. In extraction DWT technique is applied on both host image and watermarked image. Finally reverse process of embedding is applied to extract exact watermark image.

A secure non-blind block based digital image watermarking technique using DWT and DCT

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.

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A New CAD System for Breast Microcalcifications Diagnosis

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.

Breast cancer detection: A review on mammograms analysis techniques

In this paper we make contributions to a non-blind multiple watermarking scheme that proceeds by embedding a binary image in the discrete wavelet transform bands of a gray scale image. Unlike the common wavelet based watermarking techniques, the proposed scheme lies essentially on marking the approximation and diagonal bands of the discrete wavelet transform (DWT) of the cover image achieving a better compromise between fidelity and robustness. Experiments show that our contributions provide the multiple watermarking scheme with robustness to a wide variety of attacks.

A robust multiple watermarking scheme based on the DWT

Segmentation of cerebrovascular structures from MRA (Magnetic Resonance Angiography) is a challenging assignment as a result of the complexity structures of the vessels and the characteristics of the MRA images. This article presents a new method which extracts the vascular structures from 2D medical images and helps the doctors in the treatment and diagnosis of vascular disease. In our proposed method, the Hessian-based multiscale filtering is firstly used to enhance vascular structures. And then, the blood vessels are extracted by executing the local entropy. Our method was tested on MRA database. Experimental results on MRA images demonstrate the ability to extract the most of the vascular structures successfully.

Vascular tree segmentation in MRA images using Hessian-based multiscale filtering and local entropy thresholding

Blood vessels in Magnetic Resonance Angiography (MRA) image plays an important role in medical diagnosis of divers diseases. Cerebrovascular accident (CVA) is the main cause of death. The three dimensional segmentation of MRA images is helpful for the detection of the CVA in early stage. Due to the low contrast of thin vessels, loud noise and the complex structure of vessels, it is difficult to extract vessels from MRA images precisely. In this paper, we present a new method of segmentation. The proposed algorithm contains two major steps: Firstly, the Contrast Limited Adaptative Histogram Equalization (CLAHE) method is applied to enhance the image. Then, the vesselness filter is used to extract the blood vessels. Our method was tested and evaluated on 3D MRA database. It demonstrates the ability to extract the most of the vascular structures successfully. The accuracy of the proposed method reaches more than 95% which was higher than the recent methods.

Hamrouni Kamel

Papers

Breast cancer detection: A review on mammograms analysis techniques

A robust multiple watermarking scheme based on the DWT

Vascular tree segmentation in MRA images using Hessian-based multiscale filtering and local entropy thresholding

3D MRA segmentation using the vesselness filter