Computer-aided diagnosis of breast cancer based on fine needle biopsy microscopic images

Kowal, Marek; Filipczuk, Paweł; Obuchowicz, Andrzej; Korbicz, Józef; Monczak, Roman

doi:10.1016/j.compbiomed.2013.08.003

Cited by 229 publications

(83 citation statements)

References 20 publications

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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%

“…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%

“…HRIs, including radiological photos, clinical images, etc., are important data sources for diagnosis, consultation, and case discussion [1][2][3][4]. In the Internet age, doctors wish to conveniently access images and have discussions with other doctors via e-whiteboard communication [5,6].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Medical high-resolution image sharing and electronic whiteboard system: A pure-web-based system for accessing and discussing lossless original images in telemedicine

Qiao¹,

Liu²,

Chen³

et al. 2015

Computer Methods and Programs in Biomedicine

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Section: Hris Tiling With Unbalanced Ratio Scale Pyramid Structure (Umentioning

confidence: 99%

Section: Hris Tiling With Unbalanced Ratio Scale Pyramid Structure (Umentioning

confidence: 99%

See 1 more Smart Citation

Medical high-resolution image sharing and electronic whiteboard system: A pure-web-based system for accessing and discussing lossless original images in telemedicine

Qiao¹,

Liu²,

Chen³

et al. 2015

Computer Methods and Programs in Biomedicine

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“…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%

iMAGE cloud: medical image processing as a service for regional healthcare in a hybrid cloud environment

Liu

Chen

Nie

et al. 2016

Environ Health Prev Med

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Objectives To handle the emergence of the regional healthcare ecosystem, physicians and surgeons in various departments and healthcare institutions must process medical images securely, conveniently, and efficiently, and must integrate them with electronic medical records (EMRs). In this manuscript, we propose a software as a service (SaaS) cloud called the iMAGE cloud. Methods A three-layer hybrid cloud was created to provide medical image processing services in the smart city of Wuxi, China, in April 2015. In the first step, medical images and EMR data were received and integrated via the hybrid regional healthcare network. Then, traditional and advanced image processing functions were proposed and computed in a unified manner in the high-performance cloud units. Finally, the image processing results were delivered to regional users using the virtual desktop infrastructure (VDI) technology. Security infrastructure was also taken into consideration.Results Integrated information query and many advanced medical image processing functions-such as coronary extraction, pulmonary reconstruction, vascular extraction, intelligent detection of pulmonary nodules, image fusion, and 3D printing-were available to local physicians and surgeons in various departments and healthcare institutions.Conclusions Implementation results indicate that the iMAGE cloud can provide convenient, efficient, compatible, and secure medical image processing services in regional healthcare networks. The iMAGE cloud has been proven to be valuable in applications in the regional healthcare system, and it could have a promising future in the healthcare system worldwide.

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“…[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%

Fuzzy C-Means in Finding Subtypes of Cancers in Cancer Database

Kannan

Ramthilagam²,

Devi

et al. 2014

J. Innov. Opt. Health Sci.

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Finding subtypes of cancer in breast cancer database is an extremely di±cult task because of heavy noise by measurement error. Most of the recent clustering techniques for breast cancer database to achieve cancerous and noncancerous often weigh down the interpretability of the structure. Hence, this paper tries to¯nd e®ective Fuzzy C-Means-based clustering techniques to identify the proper subtypes of cancer in breast cancer database. This paper obtains the objective function of e®ective Fuzzy C-Means clustering techniques by incorporating the kernel induced distance function, Renyi's entropy function, weighted distance measure and neighborhood termsbased spatial context. The e®ectiveness of the proposed methods are proved through the experimental works on Lung cancer database, IRIS dataset, Wine dataset, Checkerboard dataset, Time Series dataset and Yeast dataset. Finally, the proposed methods are implemented successfully to cluster the breast cancer database into cancerous and noncancerous. The clustering accuracy has been validated through error matrix and silhouette method.

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Computer-aided diagnosis of breast cancer based on fine needle biopsy microscopic images

Cited by 229 publications

References 20 publications

Medical high-resolution image sharing and electronic whiteboard system: A pure-web-based system for accessing and discussing lossless original images in telemedicine

Medical high-resolution image sharing and electronic whiteboard system: A pure-web-based system for accessing and discussing lossless original images in telemedicine

iMAGE cloud: medical image processing as a service for regional healthcare in a hybrid cloud environment

Fuzzy C-Means in Finding Subtypes of Cancers in Cancer Database

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