<![CDATA[Digital images are essential visual representations in medical analysis, particularly for detecting tuberculoma, a severe tuberculosis complication with high mortality. This study aims to enhance the quality of MRI T1 images for identifying specific tuberculoma areas by comparing four method segmentation, thresholding, negation, and embossed. The dataset comprises T1 MRI brain scans in top and bottom positions, followed by pre-processing stages to reduce noise using gaussian blurring, median blurring, and sharpening techniques. The pre-processing results indicate all methods retain image details effectively. Further analysis reveals the Embossed method produces the clearest images with high contrast, facilitating tuberculoma identification. The advantage of this method lies in its ability to highlight structural details through a three-dimensional effect. The findings conclude that the embossed method effectively improves the accuracy of tuberculoma detection, contributing significantly to advancing medical image analysis techniques. This research is expected to positively impact imaging-based disease diagnosis, particularly in brain tumor cases like tuberculoma.]]>
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