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https://www.selleckchem.com/products/a-1210477.html Breast cancer is one of the leading causes of mortality in the world and it occurs in high frequency among women that carries away many lives. To detect cancer, extraction or segmentation of lesions/tumors is required. Segmentation process is very crucial if the mammogram images are blurred or low contrast. This paper suggests a novel clustering approach for segmenting lesions/tumors in the mammogram images using Atanassov's intuitionistic fuzzy set theory. The algorithm initially converts an image to an intuitionistic fuzzy image using a novel intuitionistic fuzzy generator. From the intuitionistic fuzzy image, two membership intervals are computed. Then, using Zadeh's min t-conorm, a new membership function is computed. Using the new membership function, an interval type 2 fuzzy image is constructed. Two types of distance functions are used in clustering-intuitionistic fuzzy divergence and a fuzzy exponential type distance function. Further, in each iteration, membership matrix is updated using a hesitation degree and a clustered image is obtained. Tumors/lesions are then segmented from the clustered image. The proposed method is compared with existing methods both quantitatively and qualitatively and it is observed that the proposed method performs better than the existing methods. Despite the increasing use of adjuvant bone-modifying agents (BMAs) such as zoledronate and clodronate in the treatment of patients with early stage breast cancer (EBC), little is known about real world practice patterns. A physician survey was performed to address this deficit and determine interest in clinical trials of alternative strategies for BMA administration. Canadian oncologists treating patients with EBC were surveyed via an anonymized online survey. The survey collected information on physician demographics, knowledge and interpretation of adjuvant bisphosphonate guidelines, and real world prescribing practices. Questions

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