2013
DOI: 10.1016/j.compbiomed.2013.08.003
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Computer-aided diagnosis of breast cancer based on fine needle biopsy microscopic images

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Cited by 242 publications
(86 citation statements)
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“…Step 3 Calculate the segmentation module size of the LayerIndexth layer, described as segModSize(Li) = [smW(Li), smH(Li)] T , smW(Li) and smH(Li) denote the width and height of the segmentation module in the LayerIndexth layer, determined according to formula (3).…”
Section: Hris Tiling With Unbalanced Ratio Scale Pyramid Structure (Umentioning
confidence: 99%
See 2 more Smart Citations
“…Step 3 Calculate the segmentation module size of the LayerIndexth layer, described as segModSize(Li) = [smW(Li), smH(Li)] T , smW(Li) and smH(Li) denote the width and height of the segmentation module in the LayerIndexth layer, determined according to formula (3).…”
Section: Hris Tiling With Unbalanced Ratio Scale Pyramid Structure (Umentioning
confidence: 99%
“…For layer 1(1st layer, LayerIndex = 1), the Layer factor was 4 according to Formula (2), the Segmentation module size of layer 1 was 512 × 512 according to Formula (3), and the zoom factor of x-axis and y-axis was 1.50 and 1.00, respectively, according to Formula (4). Because Segmentation module size can be used to divide the original image into segmentation images (SIs) and shrink the SIs into tiled images (standard module size), hence, the Number of tiles of the 1st layer was 2 × 1 according to Formula (5), the Size of the 1st layer full image was obtained as 256 × 128 according to Formula (6).…”
Section: Hris Tiling With Unbalanced Ratio Scale Pyramid Structure (Umentioning
confidence: 99%
See 1 more Smart Citation
“…Medical image processing, a source of core innovation in medical imaging, has developed rapidly owing to the integration of applications in diagnostics [1], treatment planning [2], and clinical study [3]. To handle the emergence of the regional healthcare ecosystem [4], physicians and surgeons in various departments and healthcare institutions must process medical images securely, conveniently, and efficiently, and must share electronic medical records (EMRs) of patients with each other.…”
Section: Introductionmentioning
confidence: 99%
“…[6][7][8] Recently unsupervised clustering method of Fuzzy C-means is the most widely used method in clustering cancer medical databases. 9 Unsupervised clustering divides the dataset into several clusters based on the similarity between the data objects, it does not require any prior information about the data objects for clustering them into available structures. [10][11][12] Due to the uncertain nature of many practical real world problems, fuzzy set theory 13 based Fuzzy clustering techniques 10,14 have been proposed by researchers.…”
Section: Introductionmentioning
confidence: 99%