Morphological and statistical investigation of biofilm images may be even more critical than the image acquisition itself, in particular in the presence of morphologically complex distributions, due to the unavoidable impact of the measurement technique too. Hence, digital image pre-processing is mandatory for reliable feature extraction and enhancement preliminary to segmentation. Also, pattern recognition in automated deep learning (both supervised and unsupervised) models often requires a preliminary effective contrast-enhancement. However, no universal consensus exists on the optimal contrast enhancement approach. This paper presents and discusses a new general, robust, reproducible, accurate and easy to implement contrast enhancement procedure, briefly named MEED-procedure, able to work on images with different bacterial coverages and biofilm structures, coming from different imaging instrumentations (herein stereomicroscope and transmission microscope). It exploits a proper succession of basic morphological operations (erosion and dilation) and a horizontal line structuring element, to minimize the impact on size and shape of the even finer bacterial features. It systematically enhances the objects of interest, without histogram stretching and/or undesirable artifacts yielded by common automated methods. The quality of the MEED-procedure is ascertained by segmentation tests which demonstrate its robustness regarding the determination of threshold and convergence of the thresholding algorithm. Extensive validation tests over a rich image database, comparison with the literature and comprehensive discussion of the conceptual background support the superiority of the MEED-procedure over the existing methods and demonstrate it is not a routine application of morphological operators.
Keywords: Biofilm images; Contrast enhancement; Highly-convergent thresholding; MEED-Procedure; Morphological operators; Segmentation tests.
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