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Measure the Effectiveness of an Innovative Scheme for Medical Imaging

Author(s): Anamika Ahirwar

Journal: International Journal of Computer Applications
ISSN 0975-8887

Volume: 37;
Issue: 2;
Start page: 1;
Date: 2012;
Original page

Keywords: Self Organizing Map(SOM) | Fuzzy C-means(FCM) | Cerebrospinal Fluid(CSF) | Central Brain Tumor Registry of the United States(CBTRUS) | The National Program of Cancer Registries(NPCR) | Surveillance | Epidemiology | and End Results(SEER)

Automatic segmentation of tumor (cancer) region in medical imaging is an extremely challenging task. This plays a significant role in cancer research and clinical practices. The segmentation technique is widely used by the radiologists to segment the input medical image into meaningful regions. In this research, an innovative method is proposed for segmenting medical images based on SOM neural network. Then associate semantics to these regions using fuzzy reasoning. A hypothesis is established for brain MRI images and mammographs for breast cancer. This paper divides into seven sections: First section illustrates a brief introduction about the paper. Second section describes the overview of the scheme. Third section illustrates the image pre-processing of medical diagnosis. Computation of statistical features is described in section fourth. Section fifth calculates Chi-Square values for brain MRI images and mammogram images. Scheme evaluation for brain MRI and mammogram image are described in sixth section. Finally conclude in section seven.
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