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In this paper we present an unsupervised automatic method for segmentation of nuclei in H&E stained breast cancer biopsy images. Colour deconvolution and morphological operations are used to preprocess the images in order to remove irrelevant structures. Candidate nuclei locations, obtained with the fast radial symmetry transform, act as markers for a marker-controlled watershed segmentation....
In recent years, shape based active contours have emerged as a natural solution to overlap resolution. However, most of these shape-based methods are limited to finding and resolving one object overlap per scene and require user intervention for model initialization. In this paper, we present a novel synergistic segmentation scheme called Active Contour for Overlap Resolution using Watershed (ACOReW)...
It has been shown that the probability to develop breast cancer is strongly correlated with the appearance of tissue in mammographic images. This appearance incorporates both greylevel and tissue pattern aspects and models of local texture information, which incorporate both greylevel and spatial aspects, can as such be related to mammographic risk assessment. Here we represent texture by the variation...
Many features used in the analysis of pathology imagery are inspired by grading features defined by clinical pathologists as important for diagnosis and characterization. A large majority of these features are features of cell nuclei; as such, there is often the desire to segment the imagery into individual nuclei prior to feature extraction and further analysis. In this paper we present an analysis...
In this work we explore the application of graph cuts techniques to the problem of finding the boundary of different breast tissue regions in mammograms. The goal of the segmentation algorithm is to see if graph cuts algorithm could separate different densities for the different breast patterns. The graph cut is applied with multi-selection of seeds label to provide the hard constraint, whereas the...
This paper presents the method of cancer localization in the breast tissue digital images. The method is implemented and tested in order to be included in the image analysis system which aim is to support a surgeon in interoperative probe of pathological areas in a breast tissue. In future it will be supplemented with information about cancerous areas acquired from dielectric maps. The idea of the...
Tissue classification in mammography can help the diagnosis of breast cancer by separating healthy tissue from lesions. We present herein the use of three texture descriptors for breast tissue segmentation purposes: the Sum Histogram, the Gray Level Co-Occurrence Matrix (GLCM) and the Local Binary Pattern (LBP). A modification of the LBP is also proposed for a better distinction of the tissues. In...
High accurate detection of mass in mammogram is critical for improving the performance and efficiency of computer-aided diagnosis (CAD) system. In this paper, we propose a novel approach to enhance the detection performance of mass in mammograms using Wavelet Transform Modulus Maximum (WTMM). First, hunt the region of interest (ROI) through the whole image and the ROI was approximately located by...
A mainstay in cancer diagnostics is the classification or grading of cell nuclei based on their appearance. While the analysis of cytological samples has been automated successfully for a long time, the complexity of histological tissue samples has prevented a reliable classification with machine vision techniques. We approach this complex problem in multiple stages, analyzing first image quality,...
Breast cancer is one of the leading causes of women death in the world. Since the causes are unknown, breast cancer cannot be prevented. Micro calcifications are the earliest signs of breast cancer and their detection is one of the most important research areas now. A novel approach for image segmentation of denser mammography images is introduced, for more accurate detection of microcalcifications...
In this paper, a new approach to detect masses in digital mammograms is presented. Firstly, preprocessing procedure is carried out to enhance the contrast between mass and surrounding tissue based on the algorithm of exponential transformation, which maps a narrow range of high grey-level values into a wider range of output levels. After preprocessing, pyramid segmentation algorithm is used to segment...
Breast skin-line estimation and breast segmentation from its background is an important pre-process in mammogram image processing and computer-aided diagnosis of breast cancer. Ultimate goal of this pre-processing task is to recognize the breast tissue region on a mammogram and limit the area to be processed into a specific target region. This will increase the efficiency and accuracy of computer-aided...
Breast density has been shown to be an independent risk factor for breast cancer. In order to segment breast parenchyma, which has been proposed as a biomarker of breast cancer risk, we present an integrated algorithm for simultaneous T1 map estimation and segmentation, using a series of magnetic resonance (MR) breast images. The advantage of using this algorithm is that the step of T1 map estimation...
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