Low contrast of Magnetic Resonance (MR) images limits the visibility of subtle structures and adversely affects the outcome of both subjective and automated diagnosis. State-of-the-art contrast boosting techniques intolerably alter inherent features of MR images. Drastic changes in brightness features, induced by post-processing are not appreciated in medical imaging as the grey level values have certain diagnostic meanings. To overcome these issues this paper proposes an algorithm that enhance the contrast of MR images while preserving the underlying features as well. This method termed as Power-law and Logarithmic Modification-based Histogram Equalization (PLMHE) partitions the histogram of the image into two sub histograms after a power-law transformation and a log compression. After a modification intended for improving the dispersion of the sub-histograms and subsequent normalization, cumulative histograms are computed. Enhanced grey level values are computed from the resultant cumulative histograms. The performance of the PLMHE algorithm is compared with traditional histogram equalization based algorithms and it has been observed from the results that PLMHE can boost the image contrast without causing dynamic range compression, a significant change in mean brightness, and contrast-overshoot.
A world hunger crisis has risen since 2019 when COVID-19 hit the world. This pandemic has shifted this generation back in time, and now it is very important to be involved in new techniques that are effective in terms of better yield with less toxins. With the rate at which the population is growing, it is expected that by the year 2050, the world population would cross 9 billion. This exponential rise would require the food production to rise by 70 to 80%. This is a matter of concern for agriculture and food industries. As the world is in the fourth industrial revolution, it is the need of the hour to embed artificial intelligence and machine learning algorithms with agriculture. This research aims to accumulate different methodologies that are present and come up with a critical analysis. These methodologies have the capability to increase the yield, predict the diseases, and even increase the safety and help enhance traceability.
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