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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....
Cervix cancer is the most common gynecological malignancy and second most common cancer among female in Malaysia after breast cancer. The objective of this study is to extract the size of nucleus and cytoplasm, as well as gray level values of cervical cells from ThinPrep images so that accurate value of those parameters can easily be obtained. An alternative approach of extracting features for Pap...
Breast cancer is the most commonly diagnosed and the second leading cause of cancer death among women. In this paper we have proposed a multi stage system for detection of microcalcification using adaptive algorithms. Conventional image processing techniques do not perform well on mammographic images. The large variation in feature size and shape reduces the effectiveness of classical fixed neighborhood...
HER-2/neu, a protein often giving higher aggressiveness in breast cancers, has been shown that if the gene is expanded for some reason, the Her-2 protein produced by the cells will be over-expressed to enhance the cancer cells reproduced ability, the prognosis will be also relatively less, too. The HER-2 immunohistochemical stained provides a simple and reliable method for pathologist in clinical...
In this paper we introduce a fast algorithm for the segmentation of breast nuclei in the microscopic images of fine needle aspiration. The algorithm depends on the color information of the nuclei to separate them from the background. It consists of two main steps. First, apply Sobel edge detection method on the blue component of the image to separate touching nuclei. Second, the green component of...
This paper presents the segmentation of cancer cells in a microscopic tissue image from breast cancer. We perform color classification using the neural network. Subsequently, morphological operations and cell size considerations are used for eliminating spike noise and separating cancer cells. The excellent segmentation results from the proposed algorithm are demonstrated with microscopic images under...
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