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Analysis and interpretation of stained tumor sections is one of the main tools in cancer diagnosis and prognosis, which is mainly carried out manually by pathologists. The avent of digital pathology provides us with the challenging opportunity to automatically analyze large amounts of these complex image data in order to draw biological conclusions from them and to study cellular and tissular phenotypes...
Mitotic count is an important diagnostic factor in breast cancer grading and prognosis. Detection of mitosis in breast histopathology images is very challenging mainly due to diffused intensities along object boundary and shape variation in different stages of mitosis. This paper demonstrates an accurate technique for detecting the mitotic cells in Hematoxyline and Eosin stained images by step by...
Mammographic risk scoring has commonly been automated by extracting a set of handcrafted features from mammograms, and relating the responses directly or indirectly to breast cancer risk. We present a method that learns a feature hierarchy from unlabeled data. When the learned features are used as the input to a simple classifier, two different tasks can be addressed: i) breast density segmentation,...
A combination of Gram-Schmidt method and cluster validation algorithm based Bayesian is proposed for nuclei segmentation on microscopic breast cancer image. Gram-Schmidt is applied to identify the cell nuclei on a microscopic breast cancer image and the cluster validation algorithm based Bayesian method is used for separating the touching nuclei. The microscopic image of the breast cancer cells are...
Cancer is a disease characterized by abnormal cell growth in the human body. Cancer is evaluated by histopathological examination, which is important for further treatment planning. The tubule formation, mitotic cell count and nuclear pleomorphism are three parameter used for cancer grading. Mitotic cell (MC) count is one of important factor in cancer diagnosis from histopathological images. MC detection...
This paper details a methodology and preliminary results for applying a hierarchy of clustering units to mammographic image data. The identification of patients with breast cancer through the detection of microcalcifications and masses is a demanding classification problem; minimal false negatives are desired while simultaneously avoiding false positives that cause unnecessary cost to patients and...
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