Journal article Open Access

Detection of Breast Cancer using MRI: A Pictorial Essay of the Image Processing Techniques

Poonam Jaglan, Dr. Rajeshwar Dass, Dr. Manoj Duhan


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    <subfield code="a">Enhancement, Magnetic Resonance Imaging, Mean Square Error, Mean Absolute Error, Peak Signal to Noise Ratio, Root Mean Square Error, Segmentation.</subfield>
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    <subfield code="p">International Journal of Computer Engineering In Research Trends (IJCERT)</subfield>
    <subfield code="v">VOLUME 6</subfield>
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    <subfield code="u">Research Scholar,Deenbandhu Chhottu Ram University of Science &amp; Technology, Murthal.</subfield>
    <subfield code="a">Poonam Jaglan, Dr. Rajeshwar Dass, Dr. Manoj Duhan</subfield>
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    <subfield code="a">Detection of Breast Cancer using MRI: A Pictorial Essay of the Image Processing Techniques</subfield>
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    <subfield code="a">&lt;p&gt;Medical imaging generates the visual representation of the interior body parts for the clinical analysis/ medical intervention. Nowadays, an advanced medical imaging technique, i.e., MRI provides acute dissection anatomical information about the human soft tissues. MRI generally suffers from poor contrast, low quality due to improper brightness &amp;amp; blurriness. So contrast manipulation is compulsively needed. Image enhancement is taken as the initial step which defines the accuracy of the result. The prime objective is to improve the visual appearance or to provide a better transform representation for future automated image processing like analysis, detection, segmentation &amp;amp; recognition. Among all the existing techniques of image enhancement, the appropriate choice must be influenced by the facts, i.e., visual perspective, modality, and climatic conditions. A trade-off between noise reduction and feature preservation of the original image depends upon the filter reconstruction ability and noise model. In this paper, four different filtering algorithms such as Median filter (MF), Gaussian filter (GF), Average filter (AF) and Wiener filter (WF) are used to compare the effects of most dominant noises in MR images by calculating the statistical parameters i.e. Mean Square Error, Peak Signal to Noise Ratio, Root Mean Square Error &amp;amp; Mean Absolute Error. Also, the noise density was gradually added to the MRI image for effective comparative analysis of the filters. Further, the proposed algorithm detected the tumor region appropriately.&lt;/p&gt;</subfield>
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