An Integrated Approach to Bacteria Structure Detection using Frangi-Thresholding Segmentation and its Impact on Analysis

Molecular characterization, classification, and other analytical tasks in microbiology require accurate segmentation of bacteria structures in microscopic images. Although bacterial shapes and textures are heterogeneous, reliable segmentation remains challenging because of low contrast, illumination variations, and variations in illumination. From microscopy scans, a pipeline for integrating image processing is presented in this work. To enhance image quality and focus on diagnostically relevant areas, an adaptive region-of-interest cropping approach is combined with Retinex enhancement, color balancing, and bi-histogram equalization. Felzenszwalb's graph-based active contour method and Otsu thresholding with Frangi filter are used then to highlight tubular bacterial structures. As a result of the Frangi filter, bacterial regions are enhanced, enabling simple thresholding to accurately delineate them. With a mean Jaccard index of 0.85, the lowest mean squared error of 0.012, and the highest probabilistic Rand index of 0.78, the Otsu thresholding with Frangi filtering achieved the highest segmentation accuracy against manual annotations of over 200 microscopic images. In bio-image informatics applications, the proposed integrated pipeline demonstrates the effectiveness of tailored filtering and thresholding techniques for robust bacterial segmentation.