Computer Assisted Grading of Follicular & Prostate Lymphoma
A Classification Based on Quality Parameters
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This book does in lighting several approaches for color texture and demarcation of biased cell nuclei, also suggests many quantitative parameters, and applied them for computer-aided prognosis of CaP & FL images. Gaussian wavelet orientation transformation of color images prior to Co-occurrence calculation for H&E-stained histopathological images considerably improved the classification performance, which has 97% sensitivity in identifying according to pathologist for FL. This approach set a benchmark in histopathology by isolating all the follicles in the image, we can use the entire tissue s...
This book does in lighting several approaches for color texture and demarcation of biased cell nuclei, also suggests many quantitative parameters, and applied them for computer-aided prognosis of CaP & FL images. Gaussian wavelet orientation transformation of color images prior to Co-occurrence calculation for H&E-stained histopathological images considerably improved the classification performance, which has 97% sensitivity in identifying according to pathologist for FL. This approach set a benchmark in histopathology by isolating all the follicles in the image, we can use the entire tissue section for diagnosis and grading of the diseased. Moreover our classification approach gives additional input to pathologist so that patients should receive appropriate therapy as soon as possible to increase their chance of re-mission and to prolong their lives. It should be noted that the automated computer assisted grading system should never be considered a replacement for pathologist. Instead, it should only be used as assistance to the decision mechanism. In case of a disagreement between the computerized system and the doctors rating, the final decision is that of the oncologist
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