Optical remote sensing, brightness preserving and contrast enhancement of medical images using histogram equalization with minimum cross-entropy-Otsu algorithm

摘要

Medical pictures play a vital role in novel diagnosis; to achieve this, the quality of these images must be improved in a way that meets the necessary standards. To deal with this matter, we have proposed an optimal new histogram equalization technique with less computational complexity. In this way, using an automated thresholding technique, the image-histogram is isolated into two sub-histograms, and to avoid undesirable effects of conventional methods which cause abnormal brightness and intensity saturation effects, we proposed a new composition of Contrast-limited adaptive histogram equalization and Brightness preserving dynamic fuzzy histogram equalization techniques with appropriate weights according to the histogram situation based on a fuzzy approach. To improve the image contrast, pixels whose luminous intensity is higher than the proposed calculated floating threshold are equalized. Then, they are combined with the lower bound of the histogram and finally the normalization is applied on them. In order to evaluate the consequences, 5 different authenticated medical image databases containing mammography, ultrasonic, Computed Tomography scan and Magnetic Resonance Imaging pictures are used. Using effective quality measurements, e.g., Peak Signal to Noise Ratio, Mean Square Error, Absolute Means Brightness Error, the Index of Structural Similarity and Measure of Enhancement, significant outcomes have been obtained in different databases. The proposed algorithms are advantageous in brightness preserving and contrast enhancement of different medical images. These methods improve the quality of medical pictures and help doctors in the diagnosis process.