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A Segmented Morphological Approach to Detect Tumor in Lung Images
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Image processing is one of most growing research area these days and now it is very much integrated with the medical and biotechnology field. Image Processing can be used to analyze different medical and MRI images to get the abnormality in the image. This abnormality can be described in terms of tumor or the patch or scare on the human body. We are presenting such an approach to detect the tumor from the lung image. In this proposed approach we have applied a series of operations, first to enhance the image and then to detect the tumor from the lung image. In this proposed approach, First of all some image enhancement and noise reduction techniques are used to enhance the image quality, after that we have applied watershed segmentation and some morphological operations to get the desired result. The algorithm has been tried on a number of different images from different angles and has always given the correct desired output.
Keywords
Contrast Stretching, Gabor Filter, Histogram Modeling, Morphological operations, Watershed Segmentation.
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